System

Abstract

A system is provided.SOLUTION: A system comprising: means for providing a user interface for inputting information of an own company and a target company; means for executing an artificial intelligence model for predicting company performance after acquisition based on the input information; means for displaying the prediction result on the user interface; means for automatically generating a transaction contract and performing a legal check based on the information and the prediction result; means for generating advice for avoiding trouble by referring to past trouble cases; and means for proposing optimal acquisition / selling matching.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] The present invention aims to resolve the uncertainty and inefficiency that arise in the process of acquiring and selling companies (hereinafter referred to as M&A). Specifically, there is a need for a means to integrate and efficiently carry out a series of processes, including predicting post-acquisition corporate performance, drafting and legally reviewing transaction contracts, providing advice to avoid problems, and proposing optimal matching. However, current systems disperse these elements and do not provide consistent support, which is extremely time-consuming for users and increases uncertainty. This reduces the success rate of M&A transactions and increases costs and risks. [Means for solving the problem]

[0005] In order to solve the above problems, the present invention proposes a comprehensive system that provides the following means.

[0006] 1. By providing a means to provide a user interface for inputting information about the company and the target company, users can easily input the necessary information into the system.

[0007] 2. Implement a method to run an AI model that predicts post-acquisition corporate performance based on input information, making it possible to visualize synergy effects based on the predicted results.

[0008] 3. Provide a means to display the prediction results in the user interface so that users can intuitively understand the results.

[0009] 4. Based on the information and prediction results, transaction contracts can be automatically generated and legally checked, thereby reducing legal risks and significantly reducing the man-hours required to create contracts.

[0010] 5. Provide a means to refer to past trouble cases and generate advice to avoid trouble. This advice allows users to avoid potential risks in advance.

[0011] 6. By providing a means to propose optimal acquisition and sale matching, we will help you find the best M&A partner and improve the success rate of transactions.

[0012] The "Synergy Connect" system, which combines these tools, can streamline the M&A process and reduce uncertainty.

[0013] A "user interface" is the part of a system that provides the screens and interactive elements for a user to enter information and view results.

[0014] An "artificial intelligence model" is a program that uses algorithms such as machine learning and deep learning to analyze data and make predictions and classifications.

[0015] "Post-acquisition corporate performance" refers to a company's financial condition and operating results after acquiring another company, and includes factors such as sales, cost reductions, and profit margins.

[0016] A "transaction agreement" is a document that clearly states the terms and legal obligations of transactions between companies, and is essential in M&A transactions.

[0017] "Legal check" is the process of verifying that a transaction agreement is legally valid and meets the necessary compliance requirements.

[0018] "Advice to avoid problems" is a suggestion or measure based on past trouble cases and data to help users avoid future risks.

[0019] "Optimal matching" refers to proposing the most effective combination for both the acquiring and selling companies, maximizing synergy effects. [Brief explanation of the drawings]

[0020] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6]FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0021] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0022] First, the terms used in the following description will be explained.

[0023] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0024] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0025] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0026] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0027] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0028] [First embodiment]

[0029] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0030] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0031] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0032] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0033] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0034] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0035] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0036] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0037] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0038] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0039] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0040] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0041] MODE FOR CARRYING OUT THE INVENTION

[0042] The present invention, "Synergy Connect," is an integrated system for efficiently and effectively conducting the M&A process. The main functions of the system and the specific program processing flow are described below.

[0043] Key System Features

[0044] 1. Enter your company information

[0045] User: Uses a terminal to enter detailed data about their own company and the target company (financial data, business operations, number of employees, etc.).

[0046] Server: Receives the entered information, validates it, and saves it in the database.

[0047] 2. AI-based corporate performance prediction

[0048] Server: Passes saved company information to the AI ​​analysis module.

[0049] AI module: It uses machine learning algorithms to analyze data and predict post-acquisition company performance, specifically, sales growth and cost savings.

[0050] Server: Receives prediction results and converts them into a data format for display on the user interface.

[0051] 3. Displaying the prediction results

[0052] Server: Formats the analysis results and sends them to the terminal in a format that is easy for the user to understand.

[0053] User: Check the prediction results on the device and decide on the next action.

[0054] 4. Automatic generation and legal check of transaction contracts

[0055] User: If you want to proceed with a contract based on the prediction results, select the automatic contract generation function.

[0056] Server: Generates a customized contract based on the template contract, reflecting user information and predicted results. In addition, the server uses the legal check module to check legal requirements.

[0057] Legal Check Module: Analyzes contract content and performs legal compliance checks.

[0058] 5. Providing advice to avoid problems

[0059] Server: Refer to the database of past problems and search for similar cases.

[0060] Server: Generates advice to avoid problems and displays it in the user interface.

[0061] 6. Proposal of optimal matching

[0062] Server: Matches optimal acquisitions and sales based on user conditions and AI analysis results.

[0063] Server: Generates a report of the matching results and delivers it to the user interface.

[0064] User: Check the proposed matching results on the device and proceed with the transaction based on the suggestions.

[0065] Specific examples

[0066] Enter your company information

[0067] The user uses a terminal to input financial data (sales, profit margin, etc.) for "Company A" and selects "Company B" as the acquisition target. The server stores this information in a database and performs validation.

[0068] AI-based business performance prediction

[0069] The server passes data from "Company A" and "Company B" to the AI ​​module, which predicts a 20% sales growth rate and a 15% cost reduction for "Company A+B" after the acquisition. The server formats the results and sends them to the user interface.

[0070] Automatic generation and legal check of trade contracts

[0071] The user checks the prediction results and selects the automatic contract generation function. The server customizes the contract based on a template and performs legal checks, such as checking that confidentiality clauses and payment terms are included correctly.

[0072] Providing advice to avoid problems

[0073] The server refers to a database of past problems and provides advice on setting strict payment terms based on information such as "cases where problems occurred under similar conditions resulted in payment delays after the contract was signed."

[0074] Proposal of optimal matching

[0075] The server proposes "Company C" as the most suitable acquisition target based on the user's conditions and the results of the AI ​​analysis. The results are displayed on the user interface, and the user confirms the proposal before proceeding with the final transaction.

[0076] It is expected that the invention, "Synergy Connect," will streamline each step of the M&A process and significantly reduce uncertainty.

[0077] The processing flow will be explained below.

[0078] Step 1:

[0079] Users access the Synergy Connect platform through their devices and enter information about their company and the target company, including details such as financial data, business operations, number of employees, and sales.

[0080] Step 2:

[0081] The server receives the information entered by the user and validates the input data. Validation checks include checking required fields and ensuring the correctness of the data format. If validation is successful, the information is saved in the database.

[0082] Step 3:

[0083] The server sends the stored company information to an artificial intelligence module, which uses machine learning algorithms to analyze the input data and predicts the company's performance after the acquisition.

[0084] Step 4:

[0085] An artificial intelligence module preprocesses the received data and converts it into a format suitable for analysis, which includes standardizing the data and imputing missing values.

[0086] Step 5:

[0087] The AI ​​module uses the pre-processed data to predict the company's performance after the acquisition, calculating, for example, sales growth rate, cost reduction effect, profit margin, etc. It generates the prediction results and returns them to the server.

[0088] Step 6:

[0089] The server receives the prediction results returned by the AI ​​module and converts them into a data format for display on the user interface. The results are formatted in a simple and intuitive format.

[0090] Step 7:

[0091] The server then sends the formatted prediction results to the user's device, where detailed analysis results, such as synergy effects and risk analysis, are displayed.

[0092] Step 8:

[0093] The user can review the prediction results on their device and decide on the next action, for example, to automatically generate a contract.

[0094] Step 9:

[0095] The server executes the automatic generation function of the transaction contract based on the user's request, and creates a customized contract based on the template contract, reflecting the user information and analysis results.

[0096] Step 10:

[0097] The server sends the automatically generated contract to the legal check module to check for legal compliance.

[0098] Step 11:

[0099] The legal check module analyzes the contents of the contract, checks for necessary legal requirements and compliance, and returns the check results to the server.

[0100] Step 12:

[0101] The server receives the results of the legal checks, modifies the contract as needed, and then sends the finalized contract to the user interface.

[0102] Step 13:

[0103] The server refers to a database of past problems to search for similar cases, generates advice for avoiding the problem, and sends it to the user.

[0104] Step 14:

[0105] The user checks advice on how to avoid problems on the device and takes the necessary measures.

[0106] Step 15:

[0107] The server performs optimal matching for acquisitions and sales based on the user's conditions and analysis results, proposes the best partners, and delivers the results to the user interface.

[0108] Step 16:

[0109] The user checks the proposed matching results on the terminal and proceeds with the transaction based on the proposed results.

[0110] Example 1

[0111] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0112] Traditional corporate acquisition and sale processes are complex and time-consuming, requiring the management of vast amounts of information, forecasting future corporate performance, and legal procedures. In particular, the creation of transaction contracts and legal checks, as well as the data analysis required for acquisition matching, require advanced expertise, placing a significant burden on companies. For this reason, a system was needed to streamline the entire acquisition process and ensure it proceeds accurately and quickly.

[0113] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0114] In this invention, the server includes means for providing a user screen for inputting information about the company and the target company, means for executing a machine learning model to predict post-acquisition corporate performance based on the input information, means for displaying the prediction results on the user screen, means for automatically generating transaction documents and conducting legal confirmation based on the information and prediction results, means for referencing a database of past cases to generate advice for problem avoidance, and means for making optimal acquisition and sale proposals. This makes it possible to efficiently carry out everything from data input in the acquisition and sale process to predictions, contract generation, legal confirmation, problem avoidance, and matching proposals all at once.

[0115] "Information about your company and the target company" refers to detailed data necessary for acquisitions and sales, such as the company's financial data, business operations, and number of employees.

[0116] The "user screen" is the interface through which users input information about their own company and the target company via a terminal and receive feedback from the system.

[0117] A "machine learning model" is an artificial intelligence system that contains algorithms for analyzing data and predicting a company's performance after an acquisition.

[0118] "Transaction Documents" means documents, including documents required for acquisition and sale agreements, that require automated generation and legal review.

[0119] "Legal Review" refers to the process of verifying that transaction documents are legally sound and compliant.

[0120] A "case database" is a database that records cases of troubles and problems that have occurred in the past.

[0121] "Advice" refers to suggestions for avoiding problems that are generated based on a database of past cases.

[0122] "Buy / Sell Proposal" is the process of recommending the most suitable trading partner based on the user's criteria and analysis results.

[0123] The present invention is an integrated system for efficiently managing the process of acquiring and selling a company. This system functions mainly through users, terminals, and servers. Each element and its specific function will be described below.

[0124] Enter your company information

[0125] User: Enters information about the company and the target company using a terminal. For example, enters financial data for "Company A" (sales of 50 million yen, profit margin of 10%, etc.), and selects "Company B" as the acquisition target.

[0126] Terminal: Generates an HTTP POST request to send the entered data to the server.

[0127] Server: Receives the entered information and validates the data. Successfully validated data is stored in a database (e.g., MySQL (registered trademark)).

[0128] AI-based business performance prediction

[0129] Server: Converts stored corporate data into the required format to be passed to the AI ​​analysis module.

[0130] AI module: Uses machine learning technology to predict post-acquisition corporate performance. For example, it calculates a sales growth rate of 20% and a cost reduction effect of 15%. This uses a general-purpose machine learning framework (e.g., TENSORFLOW (registered trademark)).

[0131] Server: Receives prediction results and converts the data into a format that can be displayed in the user interface.

[0132] Displaying prediction results

[0133] Server: Formats the prediction results and generates an HTTP response to send to the user interface.

[0134] Terminal: The received forecast result data is displayed on the user interface. For example, "The forecasted sales growth rate is 20%."

[0135] Automatic generation and legal check of trade contracts

[0136] User: Check the prediction results and select the automatic contract generation function to proceed with the transaction agreement.

[0137] Server: Uses a contract template to generate a customized contract that reflects the user's information and predicted results. This is done using a template management tool (e.g., a general API service).

[0138] Legal Check Module: Ensures that generated contracts are legally sound and that confidentiality clauses and payment terms are included correctly.

[0139] Providing advice to avoid problems

[0140] Server: Refer to the database of past problems to search for similar cases. The database used is (e.g., MySQL).

[0141] Server: Based on the search results, generate advice to avoid problems. For example, provide advice such as "We recommend that you set strict payment terms."

[0142] Proposal of optimal matching

[0143] Server: Based on the user's requirements and the results of AI analysis, the server makes optimal acquisition and sale proposals. This process uses a recommendation engine (e.g., AWS (registered trademark) SageMaker).

[0144] Server: Generates a report of the proposal results and delivers it to the user interface.

[0145] Terminal: Based on the received proposal results, it displays them on the user interface and the user decides whether to proceed with the transaction.

[0146] Prompt Sentence Examples

[0147] "Enter Company A's financial data (sales: 50 million yen, profit margin: 10%) into the system and select Company B as the acquisition target."

[0148] "Using data from companies A and B, the system predicted a 20% sales growth rate and a 15% cost reduction."

[0149] "Based on the prediction results, generate customized contracts and run legal checks, for example, to ensure confidentiality clauses and payment terms are included correctly."

[0150] "We will refer to our database of past troubles and provide advice on how to prevent payment delays. We recommend that you set strict payment terms."

[0151] "Based on the results of the system analysis, we propose Company C as the most suitable acquisition target. Please review this proposal and proceed with the transaction."

[0152] The system streamlines the process of acquiring and selling companies, making it faster, more accurate, and more efficient.

[0153] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0154] Step 1: The user starts the terminal and logs in to the Synergy Connect system. The user enters the login information (username, password) and the terminal sends it to the server. The server performs the authentication process, and if the login is successful, the dashboard is displayed.

[0155] Step 2: The user moves to the company information input screen. The terminal displays an input form for the user to enter. The user enters detailed data about their own company and the target company (e.g., sales of 50 million yen, profit margin of 10%). The terminal sends the entered data to the server as an HTTP POST request.

[0156] Step 3: The server passes the received company information to the validation module to check whether the data format and required fields are correct. If the input data is correct, the server saves it in a database (e.g. MySQL). If the validation fails, it returns an error message to the terminal.

[0157] Step 4: The server retrieves information about Company A and Company B from the database and passes it to the AI ​​analysis module. The AI ​​module uses TensorFlow to analyze the data and predict the company's performance after the acquisition. The input data is the company's financial and performance data, and the output is predicted values ​​such as sales growth rate and cost reduction effect.

[0158] Step 5: The AI ​​module generates a prediction result. For example, it calculates a sales growth rate of 20% or a cost reduction effect of 15%. The server receives the prediction result and converts it into a format (e.g., JSON) that can be displayed in the user interface.

[0159] Step 6: The server sends the converted prediction results to the terminal as an HTTP response. The terminal analyzes the received data and displays the prediction results on a dashboard or report screen. The user confirms information such as "The predicted sales growth rate is 20%."

[0160] Step 7: The user checks the prediction results and selects the automatic contract generation function to proceed with the transaction contract. The terminal sends this request to the server. The server generates a customized contract based on the contract template, reflecting the user information and prediction results. It uses a template management tool (e.g., a general API service).

[0161] Step 8: The server passes the generated contract to the legal check module to check compliance. The legal check module analyzes whether confidentiality clauses and payment terms are included. The analysis result is returned to the server as feedback such as "The contract is legally valid."

[0162] Step 9: The server searches a database of past incidents to find similar incidents. This process uses a MySQL database. The input for the database search is contract terms and company information, and the output is similar incidents and their solutions.

[0163] Step 10: The server generates advice to avoid problems based on the search results. For example, advice such as "We recommend that you set strict payment terms" is generated and sent to the terminal. The user can then adjust the contract terms by checking this advice.

[0164] Step 11: The server makes optimal acquisition and sale proposals based on the user's conditions and the results of AI analysis. It uses a recommendation engine (e.g., AWS SageMaker) to recommend the best trading partner.

[0165] Step 12: The server generates a report of the proposal results and sends it to the terminal as an HTTP response. The terminal analyzes the proposal contents and displays them on the user interface. The user confirms information such as "Company C has been proposed as the optimal acquisition target" and decides whether to proceed with the transaction.

[0166] (Application example 1)

[0167] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0168] Currently, there is a lack of systems for efficiently inputting and analyzing vehicle usage data and proposing appropriate routes when it comes to operating and managing autonomous vehicles. In particular, there is a lack of integration between elements such as vehicle performance prediction, generation of transaction contracts, legal checks, and advice on trouble avoidance, which entails a great deal of effort and risk. This has led to issues such as increased operating costs and inadequate risk management.

[0169] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0170] In this invention, the server includes means for providing a user interface for inputting information about the company and the target company, means for executing an artificial intelligence model that predicts the company's performance after the acquisition based on the input information, means for displaying the prediction results on the user interface, means for automatically generating a transaction contract and performing legal checks based on the information and the prediction results, means for referring to past trouble cases and generating advice for avoiding trouble, means for proposing optimal acquisition and sale matching, means for inputting autonomous vehicle usage data, means for predicting vehicle performance based on the usage data, and means for optimizing operation routes based on the performance prediction results.This enables an integrated system that can efficiently perform operations and management of autonomous vehicles, from data input to analysis, contract generation, trouble avoidance, and optimized operation route proposals.

[0171] A "user interface" is a screen or operating means that allows a user to input information or check the results of the system.

[0172] An "artificial intelligence model" is software that includes machine learning algorithms to make predictions and analyses based on collected data.

[0173] "Prediction results" are information indicating future performance or outcomes obtained from data analyzed by an artificial intelligence model.

[0174] A "transaction contract" is a document that describes the contents of the contract and clarifies the legal matters related to the transaction.

[0175] "Legal check" is the process of verifying that the generated transaction contract is legally sound.

[0176] The "trouble case database" is a database that collects information about troubles that have occurred in the past and the countermeasures that were taken to deal with them.

[0177] "Advice for avoiding trouble" is based on past trouble cases and provides suggestions and measures to avoid similar problems.

[0178] "Acquisition and sale matching" is the process of making proposals and recommendations to find suitable acquisition or sale targets.

[0179] "Autonomous vehicle usage data" means data about vehicle performance, such as vehicle usage hours, maintenance costs, and driving records.

[0180] "Vehicle performance" is a comprehensive performance indicator of an autonomous vehicle, including its operating efficiency, energy consumption, and maintenance status.

[0181] "Route optimization" is the process of calculating and proposing routes for autonomous vehicles to reach their destinations with the greatest efficiency.

[0182] MODE FOR CARRYING OUT THE INVENTION

[0183] The present invention, "AutoDrive Synergy Manager," is an integrated system for streamlining the operation and management of autonomous vehicles. This system has a variety of functions for data entry, analysis, prediction, contract generation, legal checks, trouble avoidance, and route optimization.

[0184] Hardware and software used

[0185] Hardware

[0186] Smartphone

[0187] server

[0188] Autonomous vehicles

[0189] software

[0190] Python: Various data processing and AI model execution

[0191] Pandas: Working with Data Frames

[0192] NumPy: Numerical Computation

[0193] scikit-learn: machine learning algorithms

[0194] python-docx: Generate contracts

[0195] System Overview

[0196] 1. User Interface

[0197] The server provides a user interface for entering autonomous vehicle usage data via a smartphone, allowing users to enter detailed information such as vehicle usage hours, maintenance costs, and driving records.

[0198] 2. Data Validation

[0199] The data entered from the smartphone is sent to the server and validated to ensure data integrity and to check for inaccuracies.

[0200] 3. Vehicle performance prediction

[0201] The server then passes the validated data to an artificial intelligence model (e.g., Linear Regression) to predict vehicle performance, including operational efficiency, energy consumption, and maintenance status.

[0202] 4. Displaying the prediction results

[0203] The predicted performance data is formatted and displayed in a user interface, allowing the user to intuitively understand the vehicle's condition.

[0204] 5. Automatic generation and legal check of transaction contracts

[0205] The server automatically generates a transaction contract based on the prediction results, and the generated contract undergoes legal checks to confirm legal compliance.

[0206] 6. Advice for avoiding trouble

[0207] The server refers to a database of past trouble cases and generates advice for avoiding troubles that have occurred under similar conditions.

[0208] 7. Route optimization

[0209] The server calculates and proposes optimal routes based on the predicted vehicle performance, thereby maximizing operational efficiency.

[0210] Specific examples

[0211] Enter company information and predict performance

[0212] For example, if data is entered indicating that the vehicle ID "V001" has been in use for 1,000 hours and that the maintenance cost is 2,000 yen, the server will predict vehicle performance based on this data. The result will be displayed as "Predicted vehicle performance score is 85."

[0213] Generate a trade agreement

[0214] Based on the prediction results, the automatic contract generation function generates a document titled "Contract Terms: Sample Terms" and performs legal checks. This contract can be viewed on the user's smartphone.

[0215] Prompt Sentence Examples

[0216] Enter the following data: "Vehicle ID: V001, Usage time: 1000 hours, Maintenance cost: 2000 yen" and let me know the predicted performance of this vehicle. Also, please automatically generate a contract.

[0217] Each step of the invention is expected to improve the efficiency of autonomous vehicle operation and management, significantly reducing operational costs and improving risk management.

[0218] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0219] Step 1:

[0220] A user uses a smartphone to input usage data for an autonomous vehicle (e.g., vehicle ID, usage time, maintenance cost) and sends it to the server. The input data includes the vehicle ID "V001," usage time "1000 hours," and maintenance cost "2000 yen." This data becomes the input information.

[0221] Step 2:

[0222] The server receives the entered data and performs data validation. Data validation checks the consistency and completeness of the entered data and eliminates inaccurate information. For example, it checks whether the usage time is a negative number or whether the maintenance cost is within a realistic range. This ensures the reliability of the data.

[0223] Step 3:

[0224] The server passes the data that has passed validation to an artificial intelligence model (Linear Regression) for analysis. Specifically, it predicts the vehicle performance score using usage time and maintenance costs as input data. The prediction model has learned from past vehicle usage data and outputs a performance score of "85" based on the input data.

[0225] Step 4:

[0226] The server displays the predicted performance score on the user interface. The user can then check the results via their smartphone. Specifically, the server displays the predicted performance score for vehicle ID: V001 as 85.

[0227] Step 5:

[0228] If the user selects automatic contract generation, the server generates a transaction contract based on the prediction results. First, the template contract is updated with the vehicle ID and predicted performance score. Next, a legal compliance check is performed to ensure, for example, that confidentiality clauses and payment terms are properly included. Finally, the generated contract is provided in a format that can be downloaded to a smartphone.

[0229] Step 6:

[0230] The server references a database of past trouble cases and generates advice for avoiding trouble under similar conditions. For example, advice such as "In a case where a problem occurred under similar conditions, there was a payment delay, so you should set strict payment terms" is provided. The results are displayed on the user interface.

[0231] Step 7:

[0232] The server calculates and proposes the optimal route based on the results of vehicle performance predictions. It calculates parameters such as usage time, maintenance costs, and operational efficiency as input data, and outputs the optimal route information. Users can check the proposed route on their smartphone and create a driving plan.

[0233] This will enable more efficient operation and management of autonomous vehicles, reducing operational costs and improving risk management.

[0234] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0235] MODE FOR CARRYING OUT THE INVENTION

[0236] The present invention, "Synergy Connect with Emotional Engine," is an integrated system for efficient and effective M&A processes, and further enhances the user experience by combining it with an emotional engine that recognizes the user's emotions.

[0237] Key System Features

[0238] 1. Enter your company information

[0239] User: Uses a terminal to enter detailed data about their own company and the target company (financial data, business operations, number of employees, etc.).

[0240] Server: Receives the entered information, validates it, and saves it in the database.

[0241] 2. AI-based corporate performance prediction

[0242] Server: Sends the stored company information to the AI ​​analysis module, which uses machine learning algorithms to analyze the data and predicts the company's performance after acquisition.

[0243] AI module: Uses machine learning algorithms to analyze data and generate predictions, such as sales growth, cost reduction, and profit margins.

[0244] Server: Receives prediction results and converts them into a data format for display on the user interface.

[0245] 3. Displaying the prediction results

[0246] Server: Formats the analysis results and sends them to the terminal in a format that is easy for the user to understand.

[0247] User: Check the prediction results on the device and decide on the next action.

[0248] 4. Emotion Recognition by Emotion Engine

[0249] User: When entering information or viewing results, the emotion engine analyzes the user's facial expressions and input data to determine their emotions, such as anxiety, excitement, or relief.

[0250] Emotion engine: Analyzes the user's emotions in real time and sends the emotional data to the server, thereby detecting the user's stress and anxiety.

[0251] 5. Emotional customization

[0252] Server: Receives emotion data from the emotion engine and customizes what is displayed and how suggestions are made, for example, if the user is feeling anxious, providing a more detailed explanation or additional information to reassure them.

[0253] 6. Automatic generation and legal check of transaction contracts

[0254] User: Select the automatic contract generation function based on the prediction results.

[0255] Server: Generates a customized contract based on the template contract, reflecting user information and analysis results. In addition, the legal check module checks for legal requirements.

[0256] Legal Check Module: Analyzes contract content and performs legal compliance checks.

[0257] 7. Providing advice to avoid problems

[0258] Server: Refer to the database of past problems and search for similar cases.

[0259] Server: Generates advice to avoid problems and displays it in the user interface.

[0260] 8. Proposal of optimal matching

[0261] Server: Matches optimal acquisitions and sales based on user conditions and analysis results.

[0262] Server: Generates a report of the matching results and delivers it to the user interface.

[0263] User: Check the proposed matching results on the device and proceed with the transaction based on the suggestions.

[0264] Specific examples

[0265] Enter your company information

[0266] The user uses a terminal to input financial data (sales, profit margin, etc.) for "Company A" and selects "Company B" as the acquisition target. The server stores this information in a database and performs validation.

[0267] AI-based business performance prediction

[0268] The server passes data from "Company A" and "Company B" to the AI ​​module, which predicts a 20% sales growth rate and a 15% cost reduction for "Company A+B" after the acquisition. The server formats the results and sends them to the user interface.

[0269] Emotion recognition and customization with emotion engine

[0270] When the user confirms the prediction, the emotion engine analyzes the user's facial expression and detects anxiety. The server receives this information and can, for example, provide more detailed explanations or follow-up questions to alleviate the user's anxiety.

[0271] Automatic generation and legal check of trade contracts

[0272] The user checks the prediction results and selects the automatic contract generation function. The server customizes the contract based on a template and performs legal checks, such as checking that confidentiality clauses and payment terms are included correctly.

[0273] Providing advice to avoid problems

[0274] The server refers to a database of past problems and provides advice on setting strict payment terms based on information such as "cases where problems occurred under similar conditions resulted in payment delays after the contract was signed."

[0275] Proposal of optimal matching

[0276] The server proposes "Company C" as the most suitable acquisition target based on the user's conditions and the results of the AI ​​analysis. The results are displayed on the user interface, and the user confirms the proposal before proceeding with the final transaction.

[0277] Our invention, "Synergy Connect with Emotional Engine," streamlines each step of the M&A process, significantly reduces uncertainty, and improves the user experience.

[0278] The processing flow will be explained below.

[0279] Step 1:

[0280] Users access the Synergy Connect platform through their devices and enter information about their company and the target company, including details such as financial data, business operations, number of employees, and sales.

[0281] Step 2:

[0282] The server receives the information entered by the user and validates the input data. Validation checks include checking required fields and ensuring the correctness of the data format. If validation is successful, the information is saved in the database.

[0283] Step 3:

[0284] The server sends the stored company information to an artificial intelligence module, which uses machine learning algorithms to analyze the data and predicts the company's performance after the acquisition.

[0285] Step 4:

[0286] An artificial intelligence module preprocesses the received data and converts it into a format suitable for analysis, which includes standardizing the data and imputing missing values.

[0287] Step 5:

[0288] The AI ​​module uses the pre-processed data to predict the company's performance after the acquisition, such as calculating sales growth rate, cost reduction effect, profit margin, etc. It generates the prediction results and returns them to the server.

[0289] Step 6:

[0290] The server receives the prediction results returned by the AI ​​module and converts them into a data format for display on the user interface. The results are formatted in a simple and intuitive format.

[0291] Step 7:

[0292] The server then sends the formatted prediction results to the user's device, where detailed analysis results, such as synergy effects and risk analysis, are displayed.

[0293] Step 8:

[0294] While the user is checking the prediction results on the device, the emotion engine analyzes the user's facial expressions and input patterns to estimate their emotional state (e.g., anxiety, excitement, relief, etc.) in real time.

[0295] Step 9:

[0296] The emotion engine analyzes the emotion data and sends it to the server. The server then customizes the content and suggestions it displays based on the emotion data it receives. For example, if the user is feeling anxious, the server will display more detailed explanations and additional support information.

[0297] Step 10:

[0298] Based on the prediction results, the user selects the automatic contract generation function.

[0299] Step 11:

[0300] The server automatically generates a transaction contract based on the user's request. Based on the template contract, it creates a customized contract that reflects the user information and analysis results.

[0301] Step 12:

[0302] The server sends the automatically generated contract to the legal check module to check for legal compliance.

[0303] Step 13:

[0304] The legal check module analyzes the contents of the contract, checks for necessary legal requirements and compliance, and returns the check results to the server.

[0305] Step 14:

[0306] The server receives the results of the legal checks, modifies the contract as needed, and then sends the finalized contract to the user interface.

[0307] Step 15:

[0308] The server references a database of past troubles to search for similar trouble cases, generates advice for avoiding the trouble, and sends it to the user's device.

[0309] Step 16:

[0310] The user checks advice on how to avoid problems on the device and takes the necessary measures.

[0311] Step 17:

[0312] The server performs optimal matching for acquisitions and sales based on the user's conditions and analysis results, proposes the best partners, and delivers the results to the user interface.

[0313] Step 18:

[0314] The user checks the proposed matching results on the terminal and proceeds with the transaction based on the proposed results.

[0315] Example 2

[0316] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0317] There is no system in place to ensure the efficient and effective execution of the M&A process. Furthermore, the lack of customization that takes user feelings into account during this process means that the user interface is not user-friendly and the user experience is not improved. Furthermore, various processes, such as verifying legal compliance of transaction contracts and providing advice on problem avoidance based on past cases, are performed manually, reducing efficiency and increasing the risk of errors.

[0318] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for providing a user interface for inputting information about the company and the target company; means for executing an artificial intelligence model to predict the company's performance after the acquisition based on the input information; means for displaying the prediction results on the user interface; means for executing an emotion engine that analyzes the user's emotions and transmits the data to the server; means for customizing the display content and proposal method based on the emotion data; means for automatically generating a transaction contract and performing legal checks based on the information and prediction results; means for generating advice to avoid problems by referring to past problem cases; and means for proposing optimal acquisition and sale matching. This makes each step of the M&A process more efficient, significantly reducing uncertainty, improving the user experience, and reducing legal risks and the probability of problems occurring.

[0319] A "user interface" is an input / output means for a user to input data and check results.

[0320] An "artificial intelligence model" is a program that uses machine learning algorithms to predict corporate performance.

[0321] An "emotion engine" is a software component that analyzes a user's emotions and provides that data.

[0322] "Validation" is the process of ensuring that entered data is accurate and appropriate.

[0323] A "database" is a system for systematically managing and retrieving stored information.

[0324] "Legal compliance" is the process of ensuring that contracts comply with relevant laws and regulations.

[0325] The "trouble case database" is a database that stores details of troubles that have occurred in the past.

[0326] "Customization" is the process of adjusting the functionality and appearance of a system based on a user's specific requirements and needs.

[0327] "Auto-generation" is the process by which the system automatically creates the necessary documents and information based on templates or existing data.

[0328] "Matching" is the process of finding and proposing companies that meet the conditions for acquisition or sale.

[0329] The present invention, "An integrated system for corporate acquisition and sale processes," is a system that efficiently and effectively supports M&A processes by combining multiple modules such as a user interface, an artificial intelligence model, an emotion engine, and a legal check module.

[0330] Key components of the system

[0331] 1. User Interface

[0332] Users use a terminal to input detailed data about their own company and the target company (e.g., financial data, business operations, number of employees, etc.). The interface is designed as an intuitive form and dashboard.

[0333] Example: A user enters the sales and profit margins of "Company A" into a form and selects "Company B" as the acquisition target.

[0334] 2. Database and Validation

[0335] The server receives the information entered by the user, performs validation to ensure the accuracy and integrity of the data, and if validation is successful, stores the information in the database.

[0336] Example: The server checks that the entered sales amount is a number and that the profit margin is within the range of 0-100%, and then saves it in the database.

[0337] 3. AI-based corporate performance prediction

[0338] The server sends the stored company information to a machine learning algorithm (e.g., TensorFlow, PyTorch) to predict how the company will perform after the acquisition. The artificial intelligence model predicts the company's profitability and growth potential.

[0339] Example: The server passes data on "Company A" and "Company B" to an AI module, which predicts a post-acquisition sales growth rate of 20% and a cost reduction effect of 15%.

[0340] 4. Displaying the prediction results

[0341] The server receives the prediction results from the AI ​​and sends them to the user's device in a format that is visually easy to understand, typically displayed as graphs or charts.

[0342] Example: The user checks the prediction results on the device and decides on the next action (e.g., further negotiation or contract generation).

[0343] 5. Emotion Recognition by Emotion Engine

[0344] As users enter information and review results, an emotion engine (e.g., OpenFace, Azure® Emotion API) analyzes the user's facial expressions in real time via the camera.

[0345] Example: When a user is reviewing a prediction, the emotion engine detects that they are feeling anxious.

[0346] 6. Emotional customization

[0347] The server receives the emotion data and customizes the content and suggestions it displays based on that data, for example providing detailed explanations or additional information if the user is feeling anxious.

[0348] Example: A server displays FAQs and past success stories to a user who is feeling unsure.

[0349] 7. Automatic generation and legal check of transaction contracts

[0350] The user selects the automatic contract generation function based on the prediction results, and the server generates a customized contract based on the template contract, reflecting the user information and analysis results.

[0351] The server passes the generated contract to a legal checking module (e.g. LegalZoom API) to verify legal compliance.

[0352] Example: Checking that server-generated contracts contain the correct confidentiality clauses and payment terms.

[0353] 8. Providing advice to avoid problems

[0354] The server refers to a database of past trouble cases, searches for similar trouble cases, and generates advice.

[0355] Example: Based on the case where a payment delay occurs, the server provides advice on how to strictly set payment terms.

[0356] 9. Proposal of optimal matching

[0357] The server searches for and proposes the best buyer or seller based on the user's criteria and the results of AI analysis, and displays the proposals on the user interface.

[0358] Example: The server proposes that "Company C is the best acquisition target," and the user confirms the proposal and proceeds with the transaction.

[0359] Prompt Sentence Examples

[0360] 1. Example of company information: "Company A's sales revenue is 500 million yen, its profit margin is 10%, and it has 100 employees. I would like to select Company B as the acquisition target."

[0361] 2. Example of emotion engine analysis: "I'm feeling anxious. View detailed explanations and success stories."

[0362] By using this type of system configuration, each step of the M&A process can be made more efficient, and customization that takes user feelings into consideration and reduction of legal risks can be made possible.

[0363] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0364] Step 1: The user enters information about their company and the target company.

[0365] Input: Detailed company information, including financial data, business operations, and number of employees.

[0366] How it works: The user uses a terminal to enter detailed information about their own company and the target company into a special input form, such as sales, profit margin, number of employees, and business details.

[0367] Output: The entered company information is sent to the server.

[0368] Step 2: The server receives and validates the entered information.

[0369] Input: Company information entered by the user.

[0370] Behavior: The server validates the field format and values ​​of the received company information, such as whether sales are numeric and whether the profit margin is between 0% and 100%.

[0371] Output: If validation is successful, the correct company information is saved in the database for passing to the next processing step, otherwise an error message is generated and returned to the user.

[0372] Step 3: The server sends the company information to the machine learning algorithm.

[0373] Input: Validated company information.

[0374] How it works: The server retrieves validated company information from a database and sends it to a machine learning algorithm (e.g., TensorFlow, PyTorch).

[0375] Output: Company information sent to the machine learning algorithm.

[0376] Step 4: The AI ​​module predicts the company's performance.

[0377] Input: Company information sent from the server.

[0378] How it works: The AI ​​module uses machine learning algorithms to predict a company's profitability and growth potential, including sales growth, cost reductions, and profit margins.

[0379] Output: Predicted company performance data after acquisition (e.g., sales growth rate of 20%, cost reduction effect of 15%).

[0380] Step 5: The server formats the prediction results into a user interface and sends them.

[0381] Input: Prediction results obtained from the AI ​​module.

[0382] How it works: The server retrieves the prediction results and formats them into a format that is visually easy for the user to understand (e.g., graphs, charts).

[0383] Output: Send the formatted prediction results to the device's user interface.

[0384] Step 6: The user checks the prediction results.

[0385] Input: A formatted prediction result display.

[0386] How it works: The user uses a device to check the prediction results on the dashboard and decide on the next action (e.g., further negotiations, generating a contract).

[0387] Output: A decision for the user's next action.

[0388] Step 7: The emotion engine analyzes the user's emotions.

[0389] Input: User facial expressions and actions.

[0390] How it works: As users enter information or check prediction results, an emotion engine (e.g., OpenFace, Azure Emotion API) analyzes the user's facial expressions in real time via the camera.

[0391] Output: User emotion data.

[0392] Step 8: The server receives the emotion data and customizes the content and suggestions displayed.

[0393] Input: Emotion data from the emotion engine.

[0394] How it works: The server analyzes the emotion data and, if the user is feeling anxious, displays detailed explanations and additional information (e.g., success stories, FAQs) in the user interface.

[0395] Output: Customized display content and suggestions.

[0396] Step 9: The user selects the automatic contract generation feature.

[0397] Input: User decision.

[0398] Operation: The user uses the terminal to select the automatic contract generation function based on the generated prediction results.

[0399] Output: A request to auto-generate a contract.

[0400] Step 10: The server creates a customized contract based on the template contract.

[0401] Input: Auto-generated request and validated company information.

[0402] Operation: The server generates a customized contract based on the template contract, reflecting user information and the results of AI analysis.

[0403] Output: A customized contract.

[0404] Step 11: The legal check module analyzes the contract and checks for legal compliance.

[0405] Input: The generated customized agreement.

[0406] How it works: A legal check module (e.g. LegalZoom API) parses the generated contract and checks for legal requirements, such as the correct inclusion of confidentiality clauses and payment terms.

[0407] Output: A legally sound contract.

[0408] Step 12: The server refers to a database of past trouble cases and generates advice for avoiding the trouble.

[0409] Input: Contract information and trouble case database.

[0410] How it works: The server searches the database for past trouble cases and generates advice based on similar trouble cases.

[0411] Output: Advice information to help you avoid problems.

[0412] Step 13: The server performs optimal matching based on the user's requirements and the results of AI analysis.

[0413] Input: User conditions and AI analysis results.

[0414] Operation: The server searches the database for buyers and sellers that meet the user's criteria and performs matching.

[0415] Output: Best buy / sell proposal report.

[0416] Step 14: The user confirms the matching results and proceeds with the transaction.

[0417] Input: Matching proposal report.

[0418] How it works: The user reviews the proposed matches on their device and makes a decision to proceed with the specific transaction.

[0419] Output: Decision to enter a trade.

[0420] In this way, servers, terminals, and users can work together to carry out the M&A process efficiently and effectively.

[0421] (Application example 2)

[0422] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0423] In the traditional M&A process, systems existed that provided functions such as predicting post-acquisition corporate performance, automatically generating transaction contracts, and conducting legal checks. However, they lacked the means to recognize user emotions and improve the user experience in real time. Furthermore, systems that match appropriate acquisitions and sales deals or provide advice to avoid problems did not take user emotions into account, potentially increasing stress and anxiety and hindering appropriate decision-making. Therefore, in the M&A process, in addition to an efficient and effective system, there was a need for a system that could recognize user emotions in real time and customize support based on that information.

[0424] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0425] In this invention, the server includes: means for providing a user interface for inputting information about the company and the target company; means for executing an artificial intelligence model that predicts the company's performance after the acquisition based on the input information; means for displaying the prediction results on the user interface; means for executing an emotion recognition engine that analyzes emotion data input from the user interface; means for customizing the display content and proposal method of the user interface based on the emotion data; means for automatically generating a transaction contract and performing legal checks based on the information and prediction results; means for generating advice to avoid problems by referring to past trouble cases; and means for proposing optimal acquisition and sale matching. This makes each step of the M&A process more efficient, significantly reduces uncertainty, and alleviates user stress and anxiety, enabling appropriate decision-making.

[0426] A "user interface" is an interface through which a user inputs information and checks results.

[0427] An "artificial intelligence model" is a model that uses machine learning algorithms to analyze data and generate predictive results.

[0428] An "emotion recognition engine" is an engine that analyzes emotions in real time from data such as a user's facial expressions and voice.

[0429] A "commercial agreement" is a legal document that sets out the agreed-upon terms and conditions of a commercial transaction.

[0430] "Legal check" is the process of verifying whether a transaction contract meets legal requirements.

[0431] The "trouble case database" is a database that records details of past troubles and their solutions.

[0432] "Matching" is the process of proposing a buyer or seller based on specific criteria.

[0433] The following system and method are used as an embodiment of this invention: The system inputs information about the company and the target company, predicts the company's performance after the acquisition, performs sentiment analysis, automatically generates transaction contracts and performs legal checks, provides advice by referring to trouble cases, and proposes optimal matching.

[0434] The system consists of the following main modules:

[0435] 1. Providing a user interface

[0436] The user uses a terminal to enter detailed data (financial data, business operations, number of employees, etc.) of their own company and the target company. This information is sent to the server, where it is validated and then stored in a database. The user interface is built using web technologies such as HTML, CSS, and JavaScript (registered trademark).

[0437] 2. AI-based corporate performance prediction

[0438] The server sends the stored company information to an AI analysis module to predict the company's performance after the acquisition. The AI ​​module uses machine learning libraries such as TensorFlow and Keras to generate predictions such as sales growth rate, cost reduction effect, and profit margin. This makes it possible to predict the company's performance.

[0439] 3. Emotion analysis using an emotion recognition engine

[0440] While the user is entering information on the device or checking prediction results, the emotion recognition engine analyzes the user's facial expressions and input data. The emotion recognition engine uses OpenCV and TensorFlow to perform image processing and emotion analysis. For example, emotions such as anxiety, excitement, and relief are detected in real time and the data is sent to the server.

[0441] 4. Emotional customization

[0442] The server receives data from the emotion engine and customizes the content and suggestions displayed in the user interface. For example, if the user is feeling anxious, it can provide more detailed explanations or additional reassuring information, thereby reducing the user's stress and anxiety.

[0443] 5. Automatic generation and legal check of transaction contracts

[0444] The user selects the automatic contract generation function based on the prediction results. The server generates a customized contract based on the template contract, reflecting the user information and analysis results, and then checks legal requirements using a legal check module. The legal check module is composed of algorithms to meet various legal regulations and compliance requirements.

[0445] 6. Providing advice to avoid problems

[0446] The server refers to a database of past trouble cases and searches for similar trouble cases. Based on this, it generates advice for avoiding trouble and displays it on the user interface. For example, based on information that "in cases where problems occurred under similar conditions, there were delays in payment after the contract was signed," it provides advice on setting strict payment terms.

[0447] 7. Proposal of optimal matching

[0448] The server proposes optimal acquisition and sale matching based on the user's conditions and analysis results. The matching results are generated in report format and delivered to the user interface. The user can confirm this and proceed with the final transaction.

[0449] Specific examples

[0450] For example, in factory operations, when a user inputs production data and the number of employees, AI predicts production efficiency and cost reduction effects. Furthermore, an emotion recognition engine can detect employee dissatisfaction and display a message on the user interface saying, "Employee dissatisfaction detected. Action required." This allows appropriate action to be taken.

[0451] Prompt Sentence Examples

[0452] "We've detected employee dissatisfaction. Would you like to suggest improvements to maintain efficiency at 85%?"

[0453] "Employee satisfaction is high. Do you want to stick with the current plan?"

[0454] "The employee's status is neutral. Would you like to consider further improvements?"

[0455] In this way, the entire system can provide highly personalized support on the user's terms, making the M&A process efficient and effective.

[0456] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0457] Step 1:

[0458] The user uses a terminal to input detailed data such as financial data, business operations, and number of employees of their own company and the target company. The input information is sent to the server, where it is validated and stored in a database. The input data validation process checks the data format and checks for required fields.

[0459] Step 2:

[0460] The server sends data to an artificial intelligence model based on the stored company information to predict the company's performance after the acquisition. Specifically, it uses machine learning libraries such as TensorFlow and Keras to generate predictions such as sales growth rate, cost reduction effect, and profit margin. These prediction results are generated in JSON format and sent to the user interface.

[0461] Step 3:

[0462] The user checks the prediction results on the device. At this stage, the user's facial expressions and voice are collected through the camera and microphone and sent to the server. The emotion recognition engine analyzes this data using OpenCV and TensorFlow to detect emotions such as anxiety, excitement, and relief in real time. The analysis results are then sent to the server.

[0463] Step 4:

[0464] The server customizes the content and suggestions displayed in the user interface based on the emotion data from the emotion recognition engine. For example, if the user feels anxious, the server can display more detailed explanations and additional information to reassure the user. This can reduce the user's stress and anxiety.

[0465] Step 5:

[0466] After checking the prediction results, the user selects the automatic generation function for the transaction contract. The server generates a customized contract based on the template contract, reflecting the user information and analysis results. Furthermore, a legal check module is used to check legal requirements. This process includes an algorithm that uses natural language processing technology to check whether each clause in the contract meets legal requirements.

[0467] Step 6:

[0468] The server refers to a database of past trouble cases and searches for similar trouble cases. For example, based on information such as "in cases where problems occurred under similar conditions, there were delays in payments after the contract was signed," it generates advice for avoiding trouble. This advice is displayed on the user interface to help the user take appropriate action.

[0469] Step 7:

[0470] The server proposes optimal acquisition and sale matching based on the user's criteria and analysis results. The matching results are generated in report format and delivered to the user interface. The user can review this and proceed with the final transaction. The matching algorithm is designed to select the optimal partner based on input data and past success stories.

[0471] Through these steps, the entire system provides highly personalized support, streamlining the M&A process and reducing the psychological burden on users.

[0472] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0473] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0474] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0475] [Second embodiment]

[0476] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0477] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0478] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0479] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0480] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0481] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0482] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0483] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0484] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0485] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0486] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0487] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0488] MODE FOR CARRYING OUT THE INVENTION

[0489] The present invention, "Synergy Connect," is an integrated system for efficiently and effectively conducting the M&A process. The main functions of the system and the specific program processing flow are described below.

[0490] Key System Features

[0491] 1. Enter your company information

[0492] User: Uses a terminal to enter detailed data about their own company and the target company (financial data, business operations, number of employees, etc.).

[0493] Server: Receives the entered information, validates it, and saves it in the database.

[0494] 2. AI-based corporate performance prediction

[0495] Server: Passes saved company information to the AI ​​analysis module.

[0496] AI module: It uses machine learning algorithms to analyze data and predict post-acquisition company performance, specifically, sales growth and cost savings.

[0497] Server: Receives prediction results and converts them into a data format for display on the user interface.

[0498] 3. Displaying the prediction results

[0499] Server: Formats the analysis results and sends them to the terminal in a format that is easy for the user to understand.

[0500] User: Check the prediction results on the device and decide on the next action.

[0501] 4. Automatic generation and legal check of transaction contracts

[0502] User: If you want to proceed with a contract based on the prediction results, select the automatic contract generation function.

[0503] Server: Generates a customized contract based on the template contract, reflecting user information and predicted results. In addition, the server uses the legal check module to check legal requirements.

[0504] Legal Check Module: Analyzes contract content and performs legal compliance checks.

[0505] 5. Providing advice to avoid problems

[0506] Server: Refer to the database of past problems and search for similar cases.

[0507] Server: Generates advice to avoid problems and displays it in the user interface.

[0508] 6. Proposal of optimal matching

[0509] Server: Matches optimal acquisitions and sales based on user conditions and AI analysis results.

[0510] Server: Generates a report of the matching results and delivers it to the user interface.

[0511] User: Check the proposed matching results on the device and proceed with the transaction based on the suggestions.

[0512] Specific examples

[0513] Enter your company information

[0514] The user uses a terminal to input financial data (sales, profit margin, etc.) for "Company A" and selects "Company B" as the acquisition target. The server stores this information in a database and performs validation.

[0515] AI-based business performance prediction

[0516] The server passes data from "Company A" and "Company B" to the AI ​​module, which predicts a 20% sales growth rate and a 15% cost reduction for "Company A+B" after the acquisition. The server formats the results and sends them to the user interface.

[0517] Automatic generation and legal check of trade contracts

[0518] The user checks the prediction results and selects the automatic contract generation function. The server customizes the contract based on a template and performs legal checks, such as checking that confidentiality clauses and payment terms are included correctly.

[0519] Providing advice to avoid problems

[0520] The server refers to a database of past problems and provides advice on setting strict payment terms based on information such as "cases where problems occurred under similar conditions resulted in payment delays after the contract was signed."

[0521] Proposal of optimal matching

[0522] The server proposes "Company C" as the most suitable acquisition target based on the user's conditions and the results of the AI ​​analysis. The results are displayed on the user interface, and the user confirms the proposal before proceeding with the final transaction.

[0523] It is expected that the invention, "Synergy Connect," will streamline each step of the M&A process and significantly reduce uncertainty.

[0524] The processing flow will be explained below.

[0525] Step 1:

[0526] Users access the Synergy Connect platform through their devices and enter information about their company and the target company, including details such as financial data, business operations, number of employees, and sales.

[0527] Step 2:

[0528] The server receives the information entered by the user and validates the input data. Validation checks include checking required fields and ensuring the correctness of the data format. If validation is successful, the information is saved in the database.

[0529] Step 3:

[0530] The server sends the stored company information to an artificial intelligence module, which uses machine learning algorithms to analyze the input data and predicts the company's performance after the acquisition.

[0531] Step 4:

[0532] An artificial intelligence module preprocesses the received data and converts it into a format suitable for analysis, which includes standardizing the data and imputing missing values.

[0533] Step 5:

[0534] The AI ​​module uses the pre-processed data to predict the company's performance after the acquisition, calculating, for example, sales growth rate, cost reduction effect, profit margin, etc. It generates the prediction results and returns them to the server.

[0535] Step 6:

[0536] The server receives the prediction results returned by the AI ​​module and converts them into a data format for display on the user interface. The results are formatted in a simple and intuitive format.

[0537] Step 7:

[0538] The server then sends the formatted prediction results to the user's device, where detailed analysis results, such as synergy effects and risk analysis, are displayed.

[0539] Step 8:

[0540] The user can review the prediction results on their device and decide on the next action, for example, to automatically generate a contract.

[0541] Step 9:

[0542] The server executes the automatic generation function of the transaction contract based on the user's request, and creates a customized contract based on the template contract, reflecting the user information and analysis results.

[0543] Step 10:

[0544] The server sends the automatically generated contract to the legal check module to check for legal compliance.

[0545] Step 11:

[0546] The legal check module analyzes the contents of the contract, checks for necessary legal requirements and compliance, and returns the check results to the server.

[0547] Step 12:

[0548] The server receives the results of the legal checks, modifies the contract as needed, and then sends the finalized contract to the user interface.

[0549] Step 13:

[0550] The server refers to a database of past problems to search for similar cases, generates advice for avoiding the problem, and sends it to the user.

[0551] Step 14:

[0552] The user checks advice on how to avoid problems on the device and takes the necessary measures.

[0553] Step 15:

[0554] The server performs optimal matching for acquisitions and sales based on the user's conditions and analysis results, proposes the best partners, and delivers the results to the user interface.

[0555] Step 16:

[0556] The user checks the proposed matching results on the terminal and proceeds with the transaction based on the proposed results.

[0557] Example 1

[0558] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0559] Traditional corporate acquisition and sale processes are complex and time-consuming, requiring the management of vast amounts of information, forecasting future corporate performance, and legal procedures. In particular, the creation of transaction contracts and legal checks, as well as the data analysis required for acquisition matching, require advanced expertise, placing a significant burden on companies. For this reason, a system was needed to streamline the entire acquisition process and ensure it proceeds accurately and quickly.

[0560] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0561] In this invention, the server includes means for providing a user screen for inputting information about the company and the target company, means for executing a machine learning model to predict post-acquisition corporate performance based on the input information, means for displaying the prediction results on the user screen, means for automatically generating transaction documents and conducting legal confirmation based on the information and prediction results, means for referencing a database of past cases to generate advice for problem avoidance, and means for making optimal acquisition and sale proposals. This makes it possible to efficiently carry out everything from data input in the acquisition and sale process to predictions, contract generation, legal confirmation, problem avoidance, and matching proposals all at once.

[0562] "Information about your company and the target company" refers to detailed data necessary for acquisitions and sales, such as the company's financial data, business operations, and number of employees.

[0563] The "user screen" is the interface through which users input information about their own company and the target company via a terminal and receive feedback from the system.

[0564] A "machine learning model" is an artificial intelligence system that contains algorithms for analyzing data and predicting a company's performance after an acquisition.

[0565] "Transaction Documents" means documents, including documents required for acquisition and sale agreements, that require automated generation and legal review.

[0566] "Legal Review" refers to the process of verifying that transaction documents are legally sound and compliant.

[0567] A "case database" is a database that records cases of troubles and problems that have occurred in the past.

[0568] "Advice" refers to suggestions for avoiding problems that are generated based on a database of past cases.

[0569] "Buy / Sell Proposal" is the process of recommending the most suitable trading partner based on the user's criteria and analysis results.

[0570] The present invention is an integrated system for efficiently managing the process of acquiring and selling a company. This system functions mainly through users, terminals, and servers. Each element and its specific function will be described below.

[0571] Enter your company information

[0572] User: Enters information about the company and the target company using a terminal. For example, enters financial data for "Company A" (sales of 50 million yen, profit margin of 10%, etc.), and selects "Company B" as the acquisition target.

[0573] Terminal: Generates an HTTP POST request to send the entered data to the server.

[0574] Server: Receives the input information, validates the data, and stores the successfully validated data in a database (e.g. MySQL).

[0575] AI-based business performance prediction

[0576] Server: Converts stored corporate data into the required format to be passed to the AI ​​analysis module.

[0577] AI module: Uses machine learning technology to predict the company's performance after the acquisition. For example, it calculates a sales growth rate of 20% and a cost reduction effect of 15%. This uses a general-purpose machine learning framework (e.g., TensorFlow).

[0578] Server: Receives prediction results and converts the data into a format that can be displayed in the user interface.

[0579] Displaying prediction results

[0580] Server: Formats the prediction results and generates an HTTP response to send to the user interface.

[0581] Terminal: The received forecast result data is displayed on the user interface. For example, "The forecasted sales growth rate is 20%."

[0582] Automatic generation and legal check of trade contracts

[0583] User: Check the prediction results and select the automatic contract generation function to proceed with the transaction agreement.

[0584] Server: Uses a contract template to generate a customized contract that reflects the user's information and predicted results. This is done using a template management tool (e.g., a general API service).

[0585] Legal Check Module: Ensures that generated contracts are legally sound and that confidentiality clauses and payment terms are included correctly.

[0586] Providing advice to avoid problems

[0587] Server: Refer to the database of past problems to search for similar cases. The database used is (e.g., MySQL).

[0588] Server: Based on the search results, generate advice to avoid problems. For example, provide advice such as "We recommend that you set strict payment terms."

[0589] Proposal of optimal matching

[0590] Server: Based on the user's requirements and the results of AI analysis, the server makes optimal acquisition and sale proposals. This process uses a recommendation engine (e.g., AWS SageMaker).

[0591] Server: Generates a report of the proposal results and delivers it to the user interface.

[0592] Terminal: Based on the received proposal results, it displays them on the user interface and the user decides whether to proceed with the transaction.

[0593] Prompt Sentence Examples

[0594] "Enter Company A's financial data (sales: 50 million yen, profit margin: 10%) into the system and select Company B as the acquisition target."

[0595] "Using data from companies A and B, the system predicted a 20% sales growth rate and a 15% cost reduction."

[0596] "Based on the prediction results, generate customized contracts and run legal checks, for example, to ensure confidentiality clauses and payment terms are included correctly."

[0597] "We will refer to our database of past troubles and provide advice on how to prevent payment delays. We recommend that you set strict payment terms."

[0598] "Based on the results of the system analysis, we propose Company C as the most suitable acquisition target. Please review this proposal and proceed with the transaction."

[0599] The system streamlines the process of acquiring and selling companies, making it faster, more accurate, and more efficient.

[0600] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0601] Step 1: The user starts the terminal and logs in to the Synergy Connect system. The user enters the login information (username, password) and the terminal sends it to the server. The server performs the authentication process, and if the login is successful, the dashboard is displayed.

[0602] Step 2: The user moves to the company information input screen. The terminal displays an input form for the user to enter. The user enters detailed data about their own company and the target company (e.g., sales of 50 million yen, profit margin of 10%). The terminal sends the entered data to the server as an HTTP POST request.

[0603] Step 3: The server passes the received company information to the validation module to check whether the data format and required fields are correct. If the input data is correct, the server saves it in a database (e.g. MySQL). If the validation fails, it returns an error message to the terminal.

[0604] Step 4: The server retrieves information about Company A and Company B from the database and passes it to the AI ​​analysis module. The AI ​​module uses TensorFlow to analyze the data and predict the company's performance after the acquisition. The input data is the company's financial and performance data, and the output is predicted values ​​such as sales growth rate and cost reduction effect.

[0605] Step 5: The AI ​​module generates a prediction result. For example, it calculates a sales growth rate of 20% or a cost reduction effect of 15%. The server receives the prediction result and converts it into a format (e.g., JSON) that can be displayed in the user interface.

[0606] Step 6: The server sends the converted prediction results to the terminal as an HTTP response. The terminal analyzes the received data and displays the prediction results on a dashboard or report screen. The user confirms information such as "The predicted sales growth rate is 20%."

[0607] Step 7: The user checks the prediction results and selects the automatic contract generation function to proceed with the transaction contract. The terminal sends this request to the server. The server generates a customized contract based on the contract template, reflecting the user information and prediction results. It uses a template management tool (e.g., a general API service).

[0608] Step 8: The server passes the generated contract to the legal check module to check compliance. The legal check module analyzes whether confidentiality clauses and payment terms are included. The analysis result is returned to the server as feedback such as "The contract is legally valid."

[0609] Step 9: The server searches a database of past incidents to find similar incidents. This process uses a MySQL database. The input for the database search is contract terms and company information, and the output is similar incidents and their solutions.

[0610] Step 10: The server generates advice to avoid problems based on the search results. For example, advice such as "We recommend that you set strict payment terms" is generated and sent to the terminal. The user can then adjust the contract terms by checking this advice.

[0611] Step 11: The server makes optimal acquisition and sale proposals based on the user's conditions and the results of AI analysis. It uses a recommendation engine (e.g., AWS SageMaker) to recommend the best trading partner.

[0612] Step 12: The server generates a report of the proposal results and sends it to the terminal as an HTTP response. The terminal analyzes the proposal contents and displays them on the user interface. The user confirms information such as "Company C has been proposed as the optimal acquisition target" and decides whether to proceed with the transaction.

[0613] (Application example 1)

[0614] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0615] Currently, there is a lack of systems for efficiently inputting and analyzing vehicle usage data and proposing appropriate routes when it comes to operating and managing autonomous vehicles. In particular, there is a lack of integration between elements such as vehicle performance prediction, generation of transaction contracts, legal checks, and advice on trouble avoidance, which entails a great deal of effort and risk. This has led to issues such as increased operating costs and inadequate risk management.

[0616] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0617] In this invention, the server includes means for providing a user interface for inputting information about the company and the target company, means for executing an artificial intelligence model that predicts the company's performance after the acquisition based on the input information, means for displaying the prediction results on the user interface, means for automatically generating a transaction contract and performing legal checks based on the information and the prediction results, means for referring to past trouble cases and generating advice for avoiding trouble, means for proposing optimal acquisition and sale matching, means for inputting autonomous vehicle usage data, means for predicting vehicle performance based on the usage data, and means for optimizing operation routes based on the performance prediction results.This enables an integrated system that can efficiently perform operations and management of autonomous vehicles, from data input to analysis, contract generation, trouble avoidance, and optimized operation route proposals.

[0618] A "user interface" is a screen or operating means that allows a user to input information or check the results of the system.

[0619] An "artificial intelligence model" is software that includes machine learning algorithms to make predictions and analyses based on collected data.

[0620] "Prediction results" are information indicating future performance or outcomes obtained from data analyzed by an artificial intelligence model.

[0621] A "transaction contract" is a document that describes the contents of the contract and clarifies the legal matters related to the transaction.

[0622] "Legal check" is the process of verifying that the generated transaction contract is legally sound.

[0623] The "trouble case database" is a database that collects information about troubles that have occurred in the past and the countermeasures that were taken to deal with them.

[0624] "Advice for avoiding trouble" is based on past trouble cases and provides suggestions and measures to avoid similar problems.

[0625] "Acquisition and sale matching" is the process of making proposals and recommendations to find suitable acquisition or sale targets.

[0626] "Autonomous vehicle usage data" means data about vehicle performance, such as vehicle usage hours, maintenance costs, and driving records.

[0627] "Vehicle performance" is a comprehensive performance indicator of an autonomous vehicle, including its operating efficiency, energy consumption, and maintenance status.

[0628] "Route optimization" is the process of calculating and proposing routes for autonomous vehicles to reach their destinations with the greatest efficiency.

[0629] MODE FOR CARRYING OUT THE INVENTION

[0630] The present invention, "AutoDrive Synergy Manager," is an integrated system for streamlining the operation and management of autonomous vehicles. This system has a variety of functions for data entry, analysis, prediction, contract generation, legal checks, trouble avoidance, and route optimization.

[0631] Hardware and software used

[0632] Hardware

[0633] Smartphone

[0634] server

[0635] Autonomous vehicles

[0636] software

[0637] Python: Various data processing and AI model execution

[0638] Pandas: Working with Data Frames

[0639] NumPy: Numerical Computation

[0640] scikit-learn: machine learning algorithms

[0641] python-docx: Generate contracts

[0642] System Overview

[0643] 1. User Interface

[0644] The server provides a user interface for entering autonomous vehicle usage data via a smartphone, allowing users to enter detailed information such as vehicle usage hours, maintenance costs, and driving records.

[0645] 2. Data Validation

[0646] The data entered from the smartphone is sent to the server and validated to ensure data integrity and to check for inaccuracies.

[0647] 3. Vehicle performance prediction

[0648] The server then passes the validated data to an artificial intelligence model (e.g., Linear Regression) to predict vehicle performance, including operational efficiency, energy consumption, and maintenance status.

[0649] 4. Displaying the prediction results

[0650] The predicted performance data is formatted and displayed in a user interface, allowing the user to intuitively understand the vehicle's condition.

[0651] 5. Automatic generation and legal check of transaction contracts

[0652] The server automatically generates a transaction contract based on the prediction results, and the generated contract undergoes legal checks to confirm legal compliance.

[0653] 6. Advice for avoiding trouble

[0654] The server refers to a database of past trouble cases and generates advice for avoiding troubles that have occurred under similar conditions.

[0655] 7. Route optimization

[0656] The server calculates and proposes optimal routes based on the predicted vehicle performance, thereby maximizing operational efficiency.

[0657] Specific examples

[0658] Enter company information and predict performance

[0659] For example, if data is entered indicating that the vehicle ID "V001" has been in use for 1,000 hours and that the maintenance cost is 2,000 yen, the server will predict vehicle performance based on this data. The result will be displayed as "Predicted vehicle performance score is 85."

[0660] Generate a trade agreement

[0661] Based on the prediction results, the automatic contract generation function generates a document titled "Contract Terms: Sample Terms" and performs legal checks. This contract can be viewed on the user's smartphone.

[0662] Prompt Sentence Examples

[0663] Enter the following data: "Vehicle ID: V001, Usage time: 1000 hours, Maintenance cost: 2000 yen" and let me know the predicted performance of this vehicle. Also, please automatically generate a contract.

[0664] Each step of the invention is expected to improve the efficiency of autonomous vehicle operation and management, significantly reducing operational costs and improving risk management.

[0665] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0666] Step 1:

[0667] A user uses a smartphone to input usage data for an autonomous vehicle (e.g., vehicle ID, usage time, maintenance cost) and sends it to the server. The input data includes the vehicle ID "V001," usage time "1000 hours," and maintenance cost "2000 yen." This data becomes the input information.

[0668] Step 2:

[0669] The server receives the entered data and performs data validation. Data validation checks the consistency and completeness of the entered data and eliminates inaccurate information. For example, it checks whether the usage time is a negative number or whether the maintenance cost is within a realistic range. This ensures the reliability of the data.

[0670] Step 3:

[0671] The server passes the data that has passed validation to an artificial intelligence model (Linear Regression) for analysis. Specifically, it predicts the vehicle performance score using usage time and maintenance costs as input data. The prediction model has learned from past vehicle usage data and outputs a performance score of "85" based on the input data.

[0672] Step 4:

[0673] The server displays the predicted performance score on the user interface. The user can then check the results via their smartphone. Specifically, the server displays the predicted performance score for vehicle ID: V001 as 85.

[0674] Step 5:

[0675] If the user selects automatic contract generation, the server generates a transaction contract based on the prediction results. First, the template contract is updated with the vehicle ID and predicted performance score. Next, a legal compliance check is performed to ensure, for example, that confidentiality clauses and payment terms are properly included. Finally, the generated contract is provided in a format that can be downloaded to a smartphone.

[0676] Step 6:

[0677] The server references a database of past trouble cases and generates advice for avoiding trouble under similar conditions. For example, advice such as "In a case where a problem occurred under similar conditions, there was a payment delay, so you should set strict payment terms" is provided. The results are displayed on the user interface.

[0678] Step 7:

[0679] The server calculates and proposes the optimal route based on the results of vehicle performance predictions. It calculates parameters such as usage time, maintenance costs, and operational efficiency as input data, and outputs the optimal route information. Users can check the proposed route on their smartphone and create a driving plan.

[0680] This will enable more efficient operation and management of autonomous vehicles, reducing operational costs and improving risk management.

[0681] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0682] MODE FOR CARRYING OUT THE INVENTION

[0683] The present invention, "Synergy Connect with Emotional Engine," is an integrated system for efficient and effective M&A processes, and further enhances the user experience by combining it with an emotional engine that recognizes the user's emotions.

[0684] Key System Features

[0685] 1. Enter your company information

[0686] User: Uses a terminal to enter detailed data about their own company and the target company (financial data, business operations, number of employees, etc.).

[0687] Server: Receives the entered information, validates it, and saves it in the database.

[0688] 2. AI-based corporate performance prediction

[0689] Server: Sends the stored company information to the AI ​​analysis module, which uses machine learning algorithms to analyze the data and predicts the company's performance after acquisition.

[0690] AI module: Uses machine learning algorithms to analyze data and generate predictions, such as sales growth, cost reduction, and profit margins.

[0691] Server: Receives prediction results and converts them into a data format for display on the user interface.

[0692] 3. Displaying the prediction results

[0693] Server: Formats the analysis results and sends them to the terminal in a format that is easy for the user to understand.

[0694] User: Check the prediction results on the device and decide on the next action.

[0695] 4. Emotion Recognition by Emotion Engine

[0696] User: When entering information or viewing results, the emotion engine analyzes the user's facial expressions and input data to determine their emotions, such as anxiety, excitement, or relief.

[0697] Emotion engine: Analyzes the user's emotions in real time and sends the emotional data to the server, thereby detecting the user's stress and anxiety.

[0698] 5. Emotional customization

[0699] Server: Receives emotion data from the emotion engine and customizes what is displayed and how suggestions are made, for example, if the user is feeling anxious, providing a more detailed explanation or additional information to reassure them.

[0700] 6. Automatic generation and legal check of transaction contracts

[0701] User: Select the automatic contract generation function based on the prediction results.

[0702] Server: Generates a customized contract based on the template contract, reflecting user information and analysis results. In addition, the legal check module checks for legal requirements.

[0703] Legal Check Module: Analyzes contract content and performs legal compliance checks.

[0704] 7. Providing advice to avoid problems

[0705] Server: Refer to the database of past problems and search for similar cases.

[0706] Server: Generates advice to avoid problems and displays it in the user interface.

[0707] 8. Proposal of optimal matching

[0708] Server: Matches optimal acquisitions and sales based on user conditions and analysis results.

[0709] Server: Generates a report of the matching results and delivers it to the user interface.

[0710] User: Check the proposed matching results on the device and proceed with the transaction based on the suggestions.

[0711] Specific examples

[0712] Enter your company information

[0713] The user uses a terminal to input financial data (sales, profit margin, etc.) for "Company A" and selects "Company B" as the acquisition target. The server stores this information in a database and performs validation.

[0714] AI-based business performance prediction

[0715] The server passes data from "Company A" and "Company B" to the AI ​​module, which predicts a 20% sales growth rate and a 15% cost reduction for "Company A+B" after the acquisition. The server formats the results and sends them to the user interface.

[0716] Emotion recognition and customization with emotion engine

[0717] When the user confirms the prediction, the emotion engine analyzes the user's facial expression and detects anxiety. The server receives this information and can, for example, provide more detailed explanations or follow-up questions to alleviate the user's anxiety.

[0718] Automatic generation and legal check of trade contracts

[0719] The user checks the prediction results and selects the automatic contract generation function. The server customizes the contract based on a template and performs legal checks, such as checking that confidentiality clauses and payment terms are included correctly.

[0720] Providing advice to avoid problems

[0721] The server refers to a database of past problems and provides advice on setting strict payment terms based on information such as "cases where problems occurred under similar conditions resulted in payment delays after the contract was signed."

[0722] Proposal of optimal matching

[0723] The server proposes "Company C" as the most suitable acquisition target based on the user's conditions and the results of the AI ​​analysis. The results are displayed on the user interface, and the user confirms the proposal before proceeding with the final transaction.

[0724] Our invention, "Synergy Connect with Emotional Engine," streamlines each step of the M&A process, significantly reduces uncertainty, and improves the user experience.

[0725] The processing flow will be explained below.

[0726] Step 1:

[0727] Users access the Synergy Connect platform through their devices and enter information about their company and the target company, including details such as financial data, business operations, number of employees, and sales.

[0728] Step 2:

[0729] The server receives the information entered by the user and validates the input data. Validation checks include checking required fields and ensuring the correctness of the data format. If validation is successful, the information is saved in the database.

[0730] Step 3:

[0731] The server sends the stored company information to an artificial intelligence module, which uses machine learning algorithms to analyze the data and predicts the company's performance after the acquisition.

[0732] Step 4:

[0733] An artificial intelligence module preprocesses the received data and converts it into a format suitable for analysis, which includes standardizing the data and imputing missing values.

[0734] Step 5:

[0735] The AI ​​module uses the pre-processed data to predict the company's performance after the acquisition, such as calculating sales growth rate, cost reduction effect, profit margin, etc. It generates the prediction results and returns them to the server.

[0736] Step 6:

[0737] The server receives the prediction results returned by the AI ​​module and converts them into a data format for display on the user interface. The results are formatted in a simple and intuitive format.

[0738] Step 7:

[0739] The server then sends the formatted prediction results to the user's device, where detailed analysis results, such as synergy effects and risk analysis, are displayed.

[0740] Step 8:

[0741] While the user is checking the prediction results on the device, the emotion engine analyzes the user's facial expressions and input patterns to estimate their emotional state (e.g., anxiety, excitement, relief, etc.) in real time.

[0742] Step 9:

[0743] The emotion engine analyzes the emotion data and sends it to the server. The server then customizes the content and suggestions it displays based on the emotion data it receives. For example, if the user is feeling anxious, the server will display more detailed explanations and additional support information.

[0744] Step 10:

[0745] Based on the prediction results, the user selects the automatic contract generation function.

[0746] Step 11:

[0747] The server automatically generates a transaction contract based on the user's request. Based on the template contract, it creates a customized contract that reflects the user information and analysis results.

[0748] Step 12:

[0749] The server sends the automatically generated contract to the legal check module to check for legal compliance.

[0750] Step 13:

[0751] The legal check module analyzes the contents of the contract, checks for necessary legal requirements and compliance, and returns the check results to the server.

[0752] Step 14:

[0753] The server receives the results of the legal checks, modifies the contract as needed, and then sends the finalized contract to the user interface.

[0754] Step 15:

[0755] The server references a database of past troubles to search for similar trouble cases, generates advice for avoiding the trouble, and sends it to the user's device.

[0756] Step 16:

[0757] The user checks advice on how to avoid problems on the device and takes the necessary measures.

[0758] Step 17:

[0759] The server performs optimal matching for acquisitions and sales based on the user's conditions and analysis results, proposes the best partners, and delivers the results to the user interface.

[0760] Step 18:

[0761] The user checks the proposed matching results on the terminal and proceeds with the transaction based on the proposed results.

[0762] Example 2

[0763] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0764] There is no system in place to ensure the efficient and effective execution of the M&A process. Furthermore, the lack of customization that takes user feelings into account during this process means that the user interface is not user-friendly and the user experience is not improved. Furthermore, various processes, such as verifying legal compliance of transaction contracts and providing advice on problem avoidance based on past cases, are performed manually, reducing efficiency and increasing the risk of errors.

[0765] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for providing a user interface for inputting information about the company and the target company; means for executing an artificial intelligence model to predict the company's performance after the acquisition based on the input information; means for displaying the prediction results on the user interface; means for executing an emotion engine that analyzes the user's emotions and transmits the data to the server; means for customizing the display content and proposal method based on the emotion data; means for automatically generating a transaction contract and performing legal checks based on the information and prediction results; means for generating advice to avoid problems by referring to past problem cases; and means for proposing optimal acquisition and sale matching. This makes each step of the M&A process more efficient, significantly reducing uncertainty, improving the user experience, and reducing legal risks and the probability of problems occurring.

[0766] A "user interface" is an input / output means for a user to input data and check results.

[0767] An "artificial intelligence model" is a program that uses machine learning algorithms to predict corporate performance.

[0768] An "emotion engine" is a software component that analyzes a user's emotions and provides that data.

[0769] "Validation" is the process of ensuring that entered data is accurate and appropriate.

[0770] A "database" is a system for systematically managing and retrieving stored information.

[0771] "Legal compliance" is the process of ensuring that contracts comply with relevant laws and regulations.

[0772] The "trouble case database" is a database that stores details of troubles that have occurred in the past.

[0773] "Customization" is the process of adjusting the functionality and appearance of a system based on a user's specific requirements and needs.

[0774] "Auto-generation" is the process by which the system automatically creates the necessary documents and information based on templates or existing data.

[0775] "Matching" is the process of finding and proposing companies that meet the conditions for acquisition or sale.

[0776] The present invention, "An integrated system for corporate acquisition and sale processes," is a system that efficiently and effectively supports M&A processes by combining multiple modules such as a user interface, an artificial intelligence model, an emotion engine, and a legal check module.

[0777] Key components of the system

[0778] 1. User Interface

[0779] Users use a terminal to input detailed data about their own company and the target company (e.g., financial data, business operations, number of employees, etc.). The interface is designed as an intuitive form and dashboard.

[0780] Example: A user enters the sales and profit margins of "Company A" into a form and selects "Company B" as the acquisition target.

[0781] 2. Database and Validation

[0782] The server receives the information entered by the user, performs validation to ensure the accuracy and integrity of the data, and if validation is successful, stores the information in the database.

[0783] Example: The server checks that the entered sales amount is a number and that the profit margin is within the range of 0-100%, and then saves it in the database.

[0784] 3. AI-based corporate performance prediction

[0785] The server sends the stored company information to a machine learning algorithm (e.g., TensorFlow, PyTorch) to predict how the company will perform after the acquisition. The artificial intelligence model predicts the company's profitability and growth potential.

[0786] Example: The server passes data on "Company A" and "Company B" to an AI module, which predicts a post-acquisition sales growth rate of 20% and a cost reduction effect of 15%.

[0787] 4. Displaying the prediction results

[0788] The server receives the prediction results from the AI ​​and sends them to the user's device in a format that is visually easy to understand, typically displayed as graphs or charts.

[0789] Example: The user checks the prediction results on the device and decides on the next action (e.g., further negotiation or contract generation).

[0790] 5. Emotion Recognition by Emotion Engine

[0791] As users enter information or check results, an emotion engine (e.g., OpenFace, Azure Emotion API) analyzes the user's facial expressions in real time through the camera.

[0792] Example: When a user is reviewing a prediction, the emotion engine detects that they are feeling anxious.

[0793] 6. Emotional customization

[0794] The server receives the emotion data and customizes the content and suggestions it displays based on that data, for example providing detailed explanations or additional information if the user is feeling anxious.

[0795] Example: A server displays FAQs and past success stories to a user who is feeling unsure.

[0796] 7. Automatic generation and legal check of transaction contracts

[0797] The user selects the automatic contract generation function based on the prediction results, and the server generates a customized contract based on the template contract, reflecting the user information and analysis results.

[0798] The server passes the generated contract to a legal checking module (e.g. LegalZoom API) to verify legal compliance.

[0799] Example: Checking that server-generated contracts contain the correct confidentiality clauses and payment terms.

[0800] 8. Providing advice to avoid problems

[0801] The server refers to a database of past trouble cases, searches for similar trouble cases, and generates advice.

[0802] Example: Based on the case where a payment delay occurs, the server provides advice on how to strictly set payment terms.

[0803] 9. Proposal of optimal matching

[0804] The server searches for and proposes the best buyer or seller based on the user's criteria and the results of AI analysis, and displays the proposals on the user interface.

[0805] Example: The server proposes that "Company C is the best acquisition target," and the user confirms the proposal and proceeds with the transaction.

[0806] Prompt Sentence Examples

[0807] 1. Example of company information: "Company A's sales revenue is 500 million yen, its profit margin is 10%, and it has 100 employees. I would like to select Company B as the acquisition target."

[0808] 2. Example of emotion engine analysis: "I'm feeling anxious. View detailed explanations and success stories."

[0809] By using this type of system configuration, each step of the M&A process can be made more efficient, and customization that takes user feelings into consideration and reduction of legal risks can be made possible.

[0810] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0811] Step 1: The user enters information about their company and the target company.

[0812] Input: Detailed company information, including financial data, business operations, and number of employees.

[0813] How it works: The user uses a terminal to enter detailed information about their own company and the target company into a special input form, such as sales, profit margin, number of employees, and business details.

[0814] Output: The entered company information is sent to the server.

[0815] Step 2: The server receives and validates the entered information.

[0816] Input: Company information entered by the user.

[0817] Behavior: The server validates the field format and values ​​of the received company information, such as whether sales are numeric and whether the profit margin is between 0% and 100%.

[0818] Output: If validation is successful, the correct company information is saved in the database for passing to the next processing step, otherwise an error message is generated and returned to the user.

[0819] Step 3: The server sends the company information to the machine learning algorithm.

[0820] Input: Validated company information.

[0821] How it works: The server retrieves validated company information from a database and sends it to a machine learning algorithm (e.g., TensorFlow, PyTorch).

[0822] Output: Company information sent to the machine learning algorithm.

[0823] Step 4: The AI ​​module predicts the company's performance.

[0824] Input: Company information sent from the server.

[0825] How it works: The AI ​​module uses machine learning algorithms to predict a company's profitability and growth potential, including sales growth, cost reductions, and profit margins.

[0826] Output: Predicted company performance data after acquisition (e.g., sales growth rate of 20%, cost reduction effect of 15%).

[0827] Step 5: The server formats the prediction results into a user interface and sends them.

[0828] Input: Prediction results obtained from the AI ​​module.

[0829] How it works: The server retrieves the prediction results and formats them into a format that is visually easy for the user to understand (e.g., graphs, charts).

[0830] Output: Send the formatted prediction results to the device's user interface.

[0831] Step 6: The user checks the prediction results.

[0832] Input: A formatted prediction result display.

[0833] How it works: The user uses a device to check the prediction results on the dashboard and decide on the next action (e.g., further negotiations, generating a contract).

[0834] Output: A decision for the user's next action.

[0835] Step 7: The emotion engine analyzes the user's emotions.

[0836] Input: User facial expressions and actions.

[0837] How it works: As users enter information or check prediction results, an emotion engine (e.g., OpenFace, Azure Emotion API) analyzes the user's facial expressions in real time via the camera.

[0838] Output: User emotion data.

[0839] Step 8: The server receives the emotion data and customizes the content and suggestions displayed.

[0840] Input: Emotion data from the emotion engine.

[0841] How it works: The server analyzes the emotion data and, if the user is feeling anxious, displays detailed explanations and additional information (e.g., success stories, FAQs) in the user interface.

[0842] Output: Customized display content and suggestions.

[0843] Step 9: The user selects the automatic contract generation feature.

[0844] Input: User decision.

[0845] Operation: The user uses the terminal to select the automatic contract generation function based on the generated prediction results.

[0846] Output: A request to auto-generate a contract.

[0847] Step 10: The server creates a customized contract based on the template contract.

[0848] Input: Auto-generated request and validated company information.

[0849] Operation: The server generates a customized contract based on the template contract, reflecting user information and the results of AI analysis.

[0850] Output: A customized contract.

[0851] Step 11: The legal check module analyzes the contract and checks for legal compliance.

[0852] Input: The generated customized agreement.

[0853] How it works: A legal check module (e.g. LegalZoom API) parses the generated contract and checks for legal requirements, such as the correct inclusion of confidentiality clauses and payment terms.

[0854] Output: A legally sound contract.

[0855] Step 12: The server refers to a database of past trouble cases and generates advice for avoiding the trouble.

[0856] Input: Contract information and trouble case database.

[0857] How it works: The server searches the database for past trouble cases and generates advice based on similar trouble cases.

[0858] Output: Advice information to help you avoid problems.

[0859] Step 13: The server performs optimal matching based on the user's requirements and the results of AI analysis.

[0860] Input: User conditions and AI analysis results.

[0861] Operation: The server searches the database for buyers and sellers that meet the user's criteria and performs matching.

[0862] Output: Best buy / sell proposal report.

[0863] Step 14: The user confirms the matching results and proceeds with the transaction.

[0864] Input: Matching proposal report.

[0865] How it works: The user reviews the proposed matches on their device and makes a decision to proceed with the specific transaction.

[0866] Output: Decision to enter a trade.

[0867] In this way, servers, terminals, and users can work together to carry out the M&A process efficiently and effectively.

[0868] (Application example 2)

[0869] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0870] In the traditional M&A process, systems existed that provided functions such as predicting post-acquisition corporate performance, automatically generating transaction contracts, and conducting legal checks. However, they lacked the means to recognize user emotions and improve the user experience in real time. Furthermore, systems that match appropriate acquisitions and sales deals or provide advice to avoid problems did not take user emotions into account, potentially increasing stress and anxiety and hindering appropriate decision-making. Therefore, in the M&A process, in addition to an efficient and effective system, there was a need for a system that could recognize user emotions in real time and customize support based on that information.

[0871] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0872] In this invention, the server includes: means for providing a user interface for inputting information about the company and the target company; means for executing an artificial intelligence model that predicts the company's performance after the acquisition based on the input information; means for displaying the prediction results on the user interface; means for executing an emotion recognition engine that analyzes emotion data input from the user interface; means for customizing the display content and proposal method of the user interface based on the emotion data; means for automatically generating a transaction contract and performing legal checks based on the information and prediction results; means for generating advice to avoid problems by referring to past trouble cases; and means for proposing optimal acquisition and sale matching. This makes each step of the M&A process more efficient, significantly reduces uncertainty, and alleviates user stress and anxiety, enabling appropriate decision-making.

[0873] A "user interface" is an interface through which a user inputs information and checks results.

[0874] An "artificial intelligence model" is a model that uses machine learning algorithms to analyze data and generate predictive results.

[0875] An "emotion recognition engine" is an engine that analyzes emotions in real time from data such as a user's facial expressions and voice.

[0876] A "commercial agreement" is a legal document that sets out the agreed-upon terms and conditions of a commercial transaction.

[0877] "Legal check" is the process of verifying whether a transaction contract meets legal requirements.

[0878] The "trouble case database" is a database that records details of past troubles and their solutions.

[0879] "Matching" is the process of proposing a buyer or seller based on specific criteria.

[0880] The following system and method are used as an embodiment of this invention: The system inputs information about the company and the target company, predicts the company's performance after the acquisition, performs sentiment analysis, automatically generates transaction contracts and performs legal checks, provides advice by referring to trouble cases, and proposes optimal matching.

[0881] The system consists of the following main modules:

[0882] 1. Providing a user interface

[0883] The user uses a terminal to input detailed data about their own company and the target company (financial data, business operations, number of employees, etc.). This information is sent to the server, validated, and then stored in a database. The user interface is built using web technologies such as HTML, CSS, and JavaScript.

[0884] 2. AI-based corporate performance prediction

[0885] The server sends the stored company information to an AI analysis module to predict the company's performance after the acquisition. The AI ​​module uses machine learning libraries such as TensorFlow and Keras to generate predictions such as sales growth rate, cost reduction effect, and profit margin. This makes it possible to predict the company's performance.

[0886] 3. Emotion analysis using an emotion recognition engine

[0887] While the user is entering information on the device or checking prediction results, the emotion recognition engine analyzes the user's facial expressions and input data. The emotion recognition engine uses OpenCV and TensorFlow to perform image processing and emotion analysis. For example, emotions such as anxiety, excitement, and relief are detected in real time and the data is sent to the server.

[0888] 4. Emotional customization

[0889] The server receives data from the emotion engine and customizes the content and suggestions displayed in the user interface. For example, if the user is feeling anxious, it can provide more detailed explanations or additional reassuring information, thereby reducing the user's stress and anxiety.

[0890] 5. Automatic generation and legal check of transaction contracts

[0891] The user selects the automatic contract generation function based on the prediction results. The server generates a customized contract based on the template contract, reflecting the user information and analysis results, and then checks legal requirements using a legal check module. The legal check module is composed of algorithms to meet various legal regulations and compliance requirements.

[0892] 6. Providing advice to avoid problems

[0893] The server refers to a database of past trouble cases and searches for similar trouble cases. Based on this, it generates advice for avoiding trouble and displays it on the user interface. For example, based on information that "in cases where problems occurred under similar conditions, there were delays in payment after the contract was signed," it provides advice on setting strict payment terms.

[0894] 7. Proposal of optimal matching

[0895] The server proposes optimal acquisition and sale matching based on the user's conditions and analysis results. The matching results are generated in report format and delivered to the user interface. The user can confirm this and proceed with the final transaction.

[0896] Specific examples

[0897] For example, in factory operations, when a user inputs production data and the number of employees, AI predicts production efficiency and cost reduction effects. Furthermore, an emotion recognition engine can detect employee dissatisfaction and display a message on the user interface saying, "Employee dissatisfaction detected. Action required." This allows appropriate action to be taken.

[0898] Prompt Sentence Examples

[0899] "We've detected employee dissatisfaction. Would you like to suggest improvements to maintain efficiency at 85%?"

[0900] "Employee satisfaction is high. Do you want to stick with the current plan?"

[0901] "The employee's status is neutral. Would you like to consider further improvements?"

[0902] In this way, the entire system can provide highly personalized support on the user's terms, making the M&A process efficient and effective.

[0903] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0904] Step 1:

[0905] The user uses a terminal to input detailed data such as financial data, business operations, and number of employees of their own company and the target company. The input information is sent to the server, where it is validated and stored in a database. The input data validation process checks the data format and checks for required fields.

[0906] Step 2:

[0907] The server sends data to an artificial intelligence model based on the stored company information to predict the company's performance after the acquisition. Specifically, it uses machine learning libraries such as TensorFlow and Keras to generate predictions such as sales growth rate, cost reduction effect, and profit margin. These prediction results are generated in JSON format and sent to the user interface.

[0908] Step 3:

[0909] The user checks the prediction results on the device. At this stage, the user's facial expressions and voice are collected through the camera and microphone and sent to the server. The emotion recognition engine analyzes this data using OpenCV and TensorFlow to detect emotions such as anxiety, excitement, and relief in real time. The analysis results are then sent to the server.

[0910] Step 4:

[0911] The server customizes the content and suggestions displayed in the user interface based on the emotion data from the emotion recognition engine. For example, if the user feels anxious, the server can display more detailed explanations and additional information to reassure the user. This can reduce the user's stress and anxiety.

[0912] Step 5:

[0913] After checking the prediction results, the user selects the automatic generation function for the transaction contract. The server generates a customized contract based on the template contract, reflecting the user information and analysis results. Furthermore, a legal check module is used to check legal requirements. This process includes an algorithm that uses natural language processing technology to check whether each clause in the contract meets legal requirements.

[0914] Step 6:

[0915] The server refers to a database of past trouble cases and searches for similar trouble cases. For example, based on information such as "in cases where problems occurred under similar conditions, there were delays in payments after the contract was signed," it generates advice for avoiding trouble. This advice is displayed on the user interface to help the user take appropriate action.

[0916] Step 7:

[0917] The server proposes optimal acquisition and sale matching based on the user's criteria and analysis results. The matching results are generated in report format and delivered to the user interface. The user can review this and proceed with the final transaction. The matching algorithm is designed to select the optimal partner based on input data and past success stories.

[0918] Through these steps, the entire system provides highly personalized support, streamlining the M&A process and reducing the psychological burden on users.

[0919] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0920] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0921] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0922] [Third embodiment]

[0923] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0924] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0925] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0926] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0927] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0928] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0929] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0930] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0931] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0932] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0933] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0934] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0935] MODE FOR CARRYING OUT THE INVENTION

[0936] The present invention, "Synergy Connect," is an integrated system for efficiently and effectively conducting the M&A process. The main functions of the system and the specific program processing flow are described below.

[0937] Key System Features

[0938] 1. Enter your company information

[0939] User: Uses a terminal to enter detailed data about their own company and the target company (financial data, business operations, number of employees, etc.).

[0940] Server: Receives the entered information, validates it, and saves it in the database.

[0941] 2. AI-based corporate performance prediction

[0942] Server: Passes saved company information to the AI ​​analysis module.

[0943] AI module: It uses machine learning algorithms to analyze data and predict post-acquisition company performance, specifically, sales growth and cost savings.

[0944] Server: Receives prediction results and converts them into a data format for display on the user interface.

[0945] 3. Displaying the prediction results

[0946] Server: Formats the analysis results and sends them to the terminal in a format that is easy for the user to understand.

[0947] User: Check the prediction results on the device and decide on the next action.

[0948] 4. Automatic generation and legal check of transaction contracts

[0949] User: If you want to proceed with a contract based on the prediction results, select the automatic contract generation function.

[0950] Server: Generates a customized contract based on the template contract, reflecting user information and predicted results. In addition, the server uses the legal check module to check legal requirements.

[0951] Legal Check Module: Analyzes contract content and performs legal compliance checks.

[0952] 5. Providing advice to avoid problems

[0953] Server: Refer to the database of past problems and search for similar cases.

[0954] Server: Generates advice to avoid problems and displays it in the user interface.

[0955] 6. Proposal of optimal matching

[0956] Server: Matches optimal acquisitions and sales based on user conditions and AI analysis results.

[0957] Server: Generates a report of the matching results and delivers it to the user interface.

[0958] User: Check the proposed matching results on the device and proceed with the transaction based on the suggestions.

[0959] Specific examples

[0960] Enter your company information

[0961] The user uses a terminal to input financial data (sales, profit margin, etc.) for "Company A" and selects "Company B" as the acquisition target. The server stores this information in a database and performs validation.

[0962] AI-based business performance prediction

[0963] The server passes data from "Company A" and "Company B" to the AI ​​module, which predicts a 20% sales growth rate and a 15% cost reduction for "Company A+B" after the acquisition. The server formats the results and sends them to the user interface.

[0964] Automatic generation and legal check of trade contracts

[0965] The user checks the prediction results and selects the automatic contract generation function. The server customizes the contract based on a template and performs legal checks, such as checking that confidentiality clauses and payment terms are included correctly.

[0966] Providing advice to avoid problems

[0967] The server refers to a database of past problems and provides advice on setting strict payment terms based on information such as "cases where problems occurred under similar conditions resulted in payment delays after the contract was signed."

[0968] Proposal of optimal matching

[0969] The server proposes "Company C" as the most suitable acquisition target based on the user's conditions and the results of the AI ​​analysis. The results are displayed on the user interface, and the user confirms the proposal before proceeding with the final transaction.

[0970] It is expected that the invention, "Synergy Connect," will streamline each step of the M&A process and significantly reduce uncertainty.

[0971] The processing flow will be explained below.

[0972] Step 1:

[0973] Users access the Synergy Connect platform through their devices and enter information about their company and the target company, including details such as financial data, business operations, number of employees, and sales.

[0974] Step 2:

[0975] The server receives the information entered by the user and validates the input data. Validation checks include checking required fields and ensuring the correctness of the data format. If validation is successful, the information is saved in the database.

[0976] Step 3:

[0977] The server sends the stored company information to an artificial intelligence module, which uses machine learning algorithms to analyze the input data and predicts the company's performance after the acquisition.

[0978] Step 4:

[0979] An artificial intelligence module preprocesses the received data and converts it into a format suitable for analysis, which includes standardizing the data and imputing missing values.

[0980] Step 5:

[0981] The AI ​​module uses the pre-processed data to predict the company's performance after the acquisition, calculating, for example, sales growth rate, cost reduction effect, profit margin, etc. It generates the prediction results and returns them to the server.

[0982] Step 6:

[0983] The server receives the prediction results returned by the AI ​​module and converts them into a data format for display on the user interface. The results are formatted in a simple and intuitive format.

[0984] Step 7:

[0985] The server then sends the formatted prediction results to the user's device, where detailed analysis results, such as synergy effects and risk analysis, are displayed.

[0986] Step 8:

[0987] The user can review the prediction results on their device and decide on the next action, for example, to automatically generate a contract.

[0988] Step 9:

[0989] The server executes the automatic generation function of the transaction contract based on the user's request, and creates a customized contract based on the template contract, reflecting the user information and analysis results.

[0990] Step 10:

[0991] The server sends the automatically generated contract to the legal check module to check for legal compliance.

[0992] Step 11:

[0993] The legal check module analyzes the contents of the contract, checks for necessary legal requirements and compliance, and returns the check results to the server.

[0994] Step 12:

[0995] The server receives the results of the legal checks, modifies the contract as needed, and then sends the finalized contract to the user interface.

[0996] Step 13:

[0997] The server refers to a database of past problems to search for similar cases, generates advice for avoiding the problem, and sends it to the user.

[0998] Step 14:

[0999] The user checks advice on how to avoid problems on the device and takes the necessary measures.

[1000] Step 15:

[1001] The server performs optimal matching for acquisitions and sales based on the user's conditions and analysis results, proposes the best partners, and delivers the results to the user interface.

[1002] Step 16:

[1003] The user checks the proposed matching results on the terminal and proceeds with the transaction based on the proposed results.

[1004] Example 1

[1005] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1006] Traditional corporate acquisition and sale processes are complex and time-consuming, requiring the management of vast amounts of information, forecasting future corporate performance, and legal procedures. In particular, the creation of transaction contracts and legal checks, as well as the data analysis required for acquisition matching, require advanced expertise, placing a significant burden on companies. For this reason, a system was needed to streamline the entire acquisition process and ensure it proceeds accurately and quickly.

[1007] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1008] In this invention, the server includes means for providing a user screen for inputting information about the company and the target company, means for executing a machine learning model to predict post-acquisition corporate performance based on the input information, means for displaying the prediction results on the user screen, means for automatically generating transaction documents and conducting legal confirmation based on the information and prediction results, means for referencing a database of past cases to generate advice for problem avoidance, and means for making optimal acquisition and sale proposals. This makes it possible to efficiently carry out everything from data input in the acquisition and sale process to predictions, contract generation, legal confirmation, problem avoidance, and matching proposals all at once.

[1009] "Information about your company and the target company" refers to detailed data necessary for acquisitions and sales, such as the company's financial data, business operations, and number of employees.

[1010] The "user screen" is the interface through which users input information about their own company and the target company via a terminal and receive feedback from the system.

[1011] A "machine learning model" is an artificial intelligence system that contains algorithms for analyzing data and predicting a company's performance after an acquisition.

[1012] "Transaction Documents" means documents, including documents required for acquisition and sale agreements, that require automated generation and legal review.

[1013] "Legal Review" refers to the process of verifying that transaction documents are legally sound and compliant.

[1014] A "case database" is a database that records cases of troubles and problems that have occurred in the past.

[1015] "Advice" refers to suggestions for avoiding problems that are generated based on a database of past cases.

[1016] "Buy / Sell Proposal" is the process of recommending the most suitable trading partner based on the user's criteria and analysis results.

[1017] The present invention is an integrated system for efficiently managing the process of acquiring and selling a company. This system functions mainly through users, terminals, and servers. Each element and its specific function will be described below.

[1018] Enter your company information

[1019] User: Enters information about the company and the target company using a terminal. For example, enters financial data for "Company A" (sales of 50 million yen, profit margin of 10%, etc.), and selects "Company B" as the acquisition target.

[1020] Terminal: Generates an HTTP POST request to send the entered data to the server.

[1021] Server: Receives the input information, validates the data, and stores the successfully validated data in a database (e.g. MySQL).

[1022] AI-based business performance prediction

[1023] Server: Converts stored corporate data into the required format to be passed to the AI ​​analysis module.

[1024] AI module: Uses machine learning technology to predict the company's performance after the acquisition. For example, it calculates a sales growth rate of 20% and a cost reduction effect of 15%. This uses a general-purpose machine learning framework (e.g., TensorFlow).

[1025] Server: Receives prediction results and converts the data into a format that can be displayed in the user interface.

[1026] Displaying prediction results

[1027] Server: Formats the prediction results and generates an HTTP response to send to the user interface.

[1028] Terminal: The received forecast result data is displayed on the user interface. For example, "The forecasted sales growth rate is 20%."

[1029] Automatic generation and legal check of trade contracts

[1030] User: Check the prediction results and select the automatic contract generation function to proceed with the transaction agreement.

[1031] Server: Uses a contract template to generate a customized contract that reflects the user's information and predicted results. This is done using a template management tool (e.g., a general API service).

[1032] Legal Check Module: Ensures that generated contracts are legally sound and that confidentiality clauses and payment terms are included correctly.

[1033] Providing advice to avoid problems

[1034] Server: Refer to the database of past problems to search for similar cases. The database used is (e.g., MySQL).

[1035] Server: Based on the search results, generate advice to avoid problems. For example, provide advice such as "We recommend that you set strict payment terms."

[1036] Proposal of optimal matching

[1037] Server: Based on the user's requirements and the results of AI analysis, the server makes optimal acquisition and sale proposals. This process uses a recommendation engine (e.g., AWS SageMaker).

[1038] Server: Generates a report of the proposal results and delivers it to the user interface.

[1039] Terminal: Based on the received proposal results, it displays them on the user interface and the user decides whether to proceed with the transaction.

[1040] Prompt Sentence Examples

[1041] "Enter Company A's financial data (sales: 50 million yen, profit margin: 10%) into the system and select Company B as the acquisition target."

[1042] "Using data from companies A and B, the system predicted a 20% sales growth rate and a 15% cost reduction."

[1043] "Based on the prediction results, generate customized contracts and run legal checks, for example, to ensure confidentiality clauses and payment terms are included correctly."

[1044] "We will refer to our database of past troubles and provide advice on how to prevent payment delays. We recommend that you set strict payment terms."

[1045] "Based on the results of the system analysis, we propose Company C as the most suitable acquisition target. Please review this proposal and proceed with the transaction."

[1046] The system streamlines the process of acquiring and selling companies, making it faster, more accurate, and more efficient.

[1047] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1048] Step 1: The user starts the terminal and logs in to the Synergy Connect system. The user enters the login information (username, password) and the terminal sends it to the server. The server performs the authentication process, and if the login is successful, the dashboard is displayed.

[1049] Step 2: The user moves to the company information input screen. The terminal displays an input form for the user to enter. The user enters detailed data about their own company and the target company (e.g., sales of 50 million yen, profit margin of 10%). The terminal sends the entered data to the server as an HTTP POST request.

[1050] Step 3: The server passes the received company information to the validation module to check whether the data format and required fields are correct. If the input data is correct, the server saves it in a database (e.g. MySQL). If the validation fails, it returns an error message to the terminal.

[1051] Step 4: The server retrieves information about Company A and Company B from the database and passes it to the AI ​​analysis module. The AI ​​module uses TensorFlow to analyze the data and predict the company's performance after the acquisition. The input data is the company's financial and performance data, and the output is predicted values ​​such as sales growth rate and cost reduction effect.

[1052] Step 5: The AI ​​module generates a prediction result. For example, it calculates a sales growth rate of 20% or a cost reduction effect of 15%. The server receives the prediction result and converts it into a format (e.g., JSON) that can be displayed in the user interface.

[1053] Step 6: The server sends the converted prediction results to the terminal as an HTTP response. The terminal analyzes the received data and displays the prediction results on a dashboard or report screen. The user confirms information such as "The predicted sales growth rate is 20%."

[1054] Step 7: The user checks the prediction results and selects the automatic contract generation function to proceed with the transaction contract. The terminal sends this request to the server. The server generates a customized contract based on the contract template, reflecting the user information and prediction results. It uses a template management tool (e.g., a general API service).

[1055] Step 8: The server passes the generated contract to the legal check module to check compliance. The legal check module analyzes whether confidentiality clauses and payment terms are included. The analysis result is returned to the server as feedback such as "The contract is legally valid."

[1056] Step 9: The server searches a database of past incidents to find similar incidents. This process uses a MySQL database. The input for the database search is contract terms and company information, and the output is similar incidents and their solutions.

[1057] Step 10: The server generates advice to avoid problems based on the search results. For example, advice such as "We recommend that you set strict payment terms" is generated and sent to the terminal. The user can then adjust the contract terms by checking this advice.

[1058] Step 11: The server makes optimal acquisition and sale proposals based on the user's conditions and the results of AI analysis. It uses a recommendation engine (e.g., AWS SageMaker) to recommend the best trading partner.

[1059] Step 12: The server generates a report of the proposal results and sends it to the terminal as an HTTP response. The terminal analyzes the proposal contents and displays them on the user interface. The user confirms information such as "Company C has been proposed as the optimal acquisition target" and decides whether to proceed with the transaction.

[1060] (Application example 1)

[1061] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1062] Currently, there is a lack of systems for efficiently inputting and analyzing vehicle usage data and proposing appropriate routes when it comes to operating and managing autonomous vehicles. In particular, there is a lack of integration between elements such as vehicle performance prediction, generation of transaction contracts, legal checks, and advice on trouble avoidance, which entails a great deal of effort and risk. This has led to issues such as increased operating costs and inadequate risk management.

[1063] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1064] In this invention, the server includes means for providing a user interface for inputting information about the company and the target company, means for executing an artificial intelligence model that predicts the company's performance after the acquisition based on the input information, means for displaying the prediction results on the user interface, means for automatically generating a transaction contract and performing legal checks based on the information and the prediction results, means for referring to past trouble cases and generating advice for avoiding trouble, means for proposing optimal acquisition and sale matching, means for inputting autonomous vehicle usage data, means for predicting vehicle performance based on the usage data, and means for optimizing operation routes based on the performance prediction results.This enables an integrated system that can efficiently perform operations and management of autonomous vehicles, from data input to analysis, contract generation, trouble avoidance, and optimized operation route proposals.

[1065] A "user interface" is a screen or operating means that allows a user to input information or check the results of the system.

[1066] An "artificial intelligence model" is software that includes machine learning algorithms to make predictions and analyses based on collected data.

[1067] "Prediction results" are information indicating future performance or outcomes obtained from data analyzed by an artificial intelligence model.

[1068] A "transaction contract" is a document that describes the contents of the contract and clarifies the legal matters related to the transaction.

[1069] "Legal check" is the process of verifying that the generated transaction contract is legally sound.

[1070] The "trouble case database" is a database that collects information about troubles that have occurred in the past and the countermeasures that were taken to deal with them.

[1071] "Advice for avoiding trouble" is based on past trouble cases and provides suggestions and measures to avoid similar problems.

[1072] "Acquisition and sale matching" is the process of making proposals and recommendations to find suitable acquisition or sale targets.

[1073] "Autonomous vehicle usage data" means data about vehicle performance, such as vehicle usage hours, maintenance costs, and driving records.

[1074] "Vehicle performance" is a comprehensive performance indicator of an autonomous vehicle, including its operating efficiency, energy consumption, and maintenance status.

[1075] "Route optimization" is the process of calculating and proposing routes for autonomous vehicles to reach their destinations with the greatest efficiency.

[1076] MODE FOR CARRYING OUT THE INVENTION

[1077] The present invention, "AutoDrive Synergy Manager," is an integrated system for streamlining the operation and management of autonomous vehicles. This system has a variety of functions for data entry, analysis, prediction, contract generation, legal checks, trouble avoidance, and route optimization.

[1078] Hardware and software used

[1079] Hardware

[1080] Smartphone

[1081] server

[1082] Autonomous vehicles

[1083] software

[1084] Python: Various data processing and AI model execution

[1085] Pandas: Working with Data Frames

[1086] NumPy: Numerical Computation

[1087] scikit-learn: machine learning algorithms

[1088] python-docx: Generate contracts

[1089] System Overview

[1090] 1. User Interface

[1091] The server provides a user interface for entering autonomous vehicle usage data via a smartphone, allowing users to enter detailed information such as vehicle usage hours, maintenance costs, and driving records.

[1092] 2. Data Validation

[1093] The data entered from the smartphone is sent to the server and validated to ensure data integrity and to check for inaccuracies.

[1094] 3. Vehicle performance prediction

[1095] The server then passes the validated data to an artificial intelligence model (e.g., Linear Regression) to predict vehicle performance, including operational efficiency, energy consumption, and maintenance status.

[1096] 4. Displaying the prediction results

[1097] The predicted performance data is formatted and displayed in a user interface, allowing the user to intuitively understand the vehicle's condition.

[1098] 5. Automatic generation and legal check of transaction contracts

[1099] The server automatically generates a transaction contract based on the prediction results, and the generated contract undergoes legal checks to confirm legal compliance.

[1100] 6. Advice for avoiding trouble

[1101] The server refers to a database of past trouble cases and generates advice for avoiding troubles that have occurred under similar conditions.

[1102] 7. Route optimization

[1103] The server calculates and proposes optimal routes based on the predicted vehicle performance, thereby maximizing operational efficiency.

[1104] Specific examples

[1105] Enter company information and predict performance

[1106] For example, if data is entered indicating that the vehicle ID "V001" has been in use for 1,000 hours and that the maintenance cost is 2,000 yen, the server will predict vehicle performance based on this data. The result will be displayed as "Predicted vehicle performance score is 85."

[1107] Generate a trade agreement

[1108] Based on the prediction results, the automatic contract generation function generates a document titled "Contract Terms: Sample Terms" and performs legal checks. This contract can be viewed on the user's smartphone.

[1109] Prompt Sentence Examples

[1110] Enter the following data: "Vehicle ID: V001, Usage time: 1000 hours, Maintenance cost: 2000 yen" and let me know the predicted performance of this vehicle. Also, please automatically generate a contract.

[1111] Each step of the invention is expected to improve the efficiency of autonomous vehicle operation and management, significantly reducing operational costs and improving risk management.

[1112] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1113] Step 1:

[1114] A user uses a smartphone to input usage data for an autonomous vehicle (e.g., vehicle ID, usage time, maintenance cost) and sends it to the server. The input data includes the vehicle ID "V001," usage time "1000 hours," and maintenance cost "2000 yen." This data becomes the input information.

[1115] Step 2:

[1116] The server receives the entered data and performs data validation. Data validation checks the consistency and completeness of the entered data and eliminates inaccurate information. For example, it checks whether the usage time is a negative number or whether the maintenance cost is within a realistic range. This ensures the reliability of the data.

[1117] Step 3:

[1118] The server passes the data that has passed validation to an artificial intelligence model (Linear Regression) for analysis. Specifically, it predicts the vehicle performance score using usage time and maintenance costs as input data. The prediction model has learned from past vehicle usage data and outputs a performance score of "85" based on the input data.

[1119] Step 4:

[1120] The server displays the predicted performance score on the user interface. The user can then check the results via their smartphone. Specifically, the server displays the predicted performance score for vehicle ID: V001 as 85.

[1121] Step 5:

[1122] If the user selects automatic contract generation, the server generates a transaction contract based on the prediction results. First, the template contract is updated with the vehicle ID and predicted performance score. Next, a legal compliance check is performed to ensure, for example, that confidentiality clauses and payment terms are properly included. Finally, the generated contract is provided in a format that can be downloaded to a smartphone.

[1123] Step 6:

[1124] The server references a database of past trouble cases and generates advice for avoiding trouble under similar conditions. For example, advice such as "In a case where a problem occurred under similar conditions, there was a payment delay, so you should set strict payment terms" is provided. The results are displayed on the user interface.

[1125] Step 7:

[1126] The server calculates and proposes the optimal route based on the results of vehicle performance predictions. It calculates parameters such as usage time, maintenance costs, and operational efficiency as input data, and outputs the optimal route information. Users can check the proposed route on their smartphone and create a driving plan.

[1127] This will enable more efficient operation and management of autonomous vehicles, reducing operational costs and improving risk management.

[1128] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1129] MODE FOR CARRYING OUT THE INVENTION

[1130] The present invention, "Synergy Connect with Emotional Engine," is an integrated system for efficient and effective M&A processes, and further enhances the user experience by combining it with an emotional engine that recognizes the user's emotions.

[1131] Key System Features

[1132] 1. Enter your company information

[1133] User: Uses a terminal to enter detailed data about their own company and the target company (financial data, business operations, number of employees, etc.).

[1134] Server: Receives the entered information, validates it, and saves it in the database.

[1135] 2. AI-based corporate performance prediction

[1136] Server: Sends the stored company information to the AI ​​analysis module, which uses machine learning algorithms to analyze the data and predicts the company's performance after acquisition.

[1137] AI module: Uses machine learning algorithms to analyze data and generate predictions, such as sales growth, cost reduction, and profit margins.

[1138] Server: Receives prediction results and converts them into a data format for display on the user interface.

[1139] 3. Displaying the prediction results

[1140] Server: Formats the analysis results and sends them to the terminal in a format that is easy for the user to understand.

[1141] User: Check the prediction results on the device and decide on the next action.

[1142] 4. Emotion Recognition by Emotion Engine

[1143] User: When entering information or viewing results, the emotion engine analyzes the user's facial expressions and input data to determine their emotions, such as anxiety, excitement, or relief.

[1144] Emotion engine: Analyzes the user's emotions in real time and sends the emotional data to the server, thereby detecting the user's stress and anxiety.

[1145] 5. Emotional customization

[1146] Server: Receives emotion data from the emotion engine and customizes what is displayed and how suggestions are made, for example, if the user is feeling anxious, providing a more detailed explanation or additional information to reassure them.

[1147] 6. Automatic generation and legal check of transaction contracts

[1148] User: Select the automatic contract generation function based on the prediction results.

[1149] Server: Generates a customized contract based on the template contract, reflecting user information and analysis results. In addition, the legal check module checks for legal requirements.

[1150] Legal Check Module: Analyzes contract content and performs legal compliance checks.

[1151] 7. Providing advice to avoid problems

[1152] Server: Refer to the database of past problems and search for similar cases.

[1153] Server: Generates advice to avoid problems and displays it in the user interface.

[1154] 8. Proposal of optimal matching

[1155] Server: Matches optimal acquisitions and sales based on user conditions and analysis results.

[1156] Server: Generates a report of the matching results and delivers it to the user interface.

[1157] User: Check the proposed matching results on the device and proceed with the transaction based on the suggestions.

[1158] Specific examples

[1159] Enter your company information

[1160] The user uses a terminal to input financial data (sales, profit margin, etc.) for "Company A" and selects "Company B" as the acquisition target. The server stores this information in a database and performs validation.

[1161] AI-based business performance prediction

[1162] The server passes data from "Company A" and "Company B" to the AI ​​module, which predicts a 20% sales growth rate and a 15% cost reduction for "Company A+B" after the acquisition. The server formats the results and sends them to the user interface.

[1163] Emotion recognition and customization with emotion engine

[1164] When the user confirms the prediction, the emotion engine analyzes the user's facial expression and detects anxiety. The server receives this information and can, for example, provide more detailed explanations or follow-up questions to alleviate the user's anxiety.

[1165] Automatic generation and legal check of trade contracts

[1166] The user checks the prediction results and selects the automatic contract generation function. The server customizes the contract based on a template and performs legal checks, such as checking that confidentiality clauses and payment terms are included correctly.

[1167] Providing advice to avoid problems

[1168] The server refers to a database of past problems and provides advice on setting strict payment terms based on information such as "cases where problems occurred under similar conditions resulted in payment delays after the contract was signed."

[1169] Proposal of optimal matching

[1170] The server proposes "Company C" as the most suitable acquisition target based on the user's conditions and the results of the AI ​​analysis. The results are displayed on the user interface, and the user confirms the proposal before proceeding with the final transaction.

[1171] Our invention, "Synergy Connect with Emotional Engine," streamlines each step of the M&A process, significantly reduces uncertainty, and improves the user experience.

[1172] The processing flow will be explained below.

[1173] Step 1:

[1174] Users access the Synergy Connect platform through their devices and enter information about their company and the target company, including details such as financial data, business operations, number of employees, and sales.

[1175] Step 2:

[1176] The server receives the information entered by the user and validates the input data. Validation checks include checking required fields and ensuring the correctness of the data format. If validation is successful, the information is saved in the database.

[1177] Step 3:

[1178] The server sends the stored company information to an artificial intelligence module, which uses machine learning algorithms to analyze the data and predicts the company's performance after the acquisition.

[1179] Step 4:

[1180] An artificial intelligence module preprocesses the received data and converts it into a format suitable for analysis, which includes standardizing the data and imputing missing values.

[1181] Step 5:

[1182] The AI ​​module uses the pre-processed data to predict the company's performance after the acquisition, such as calculating sales growth rate, cost reduction effect, profit margin, etc. It generates the prediction results and returns them to the server.

[1183] Step 6:

[1184] The server receives the prediction results returned by the AI ​​module and converts them into a data format for display on the user interface. The results are formatted in a simple and intuitive format.

[1185] Step 7:

[1186] The server then sends the formatted prediction results to the user's device, where detailed analysis results, such as synergy effects and risk analysis, are displayed.

[1187] Step 8:

[1188] While the user is checking the prediction results on the device, the emotion engine analyzes the user's facial expressions and input patterns to estimate their emotional state (e.g., anxiety, excitement, relief, etc.) in real time.

[1189] Step 9:

[1190] The emotion engine analyzes the emotion data and sends it to the server. The server then customizes the content and suggestions it displays based on the emotion data it receives. For example, if the user is feeling anxious, the server will display more detailed explanations and additional support information.

[1191] Step 10:

[1192] Based on the prediction results, the user selects the automatic contract generation function.

[1193] Step 11:

[1194] The server automatically generates a transaction contract based on the user's request. Based on the template contract, it creates a customized contract that reflects the user information and analysis results.

[1195] Step 12:

[1196] The server sends the automatically generated contract to the legal check module to check for legal compliance.

[1197] Step 13:

[1198] The legal check module analyzes the contents of the contract, checks for necessary legal requirements and compliance, and returns the check results to the server.

[1199] Step 14:

[1200] The server receives the results of the legal checks, modifies the contract as needed, and then sends the finalized contract to the user interface.

[1201] Step 15:

[1202] The server references a database of past troubles to search for similar trouble cases, generates advice for avoiding the trouble, and sends it to the user's device.

[1203] Step 16:

[1204] The user checks advice on how to avoid problems on the device and takes the necessary measures.

[1205] Step 17:

[1206] The server performs optimal matching for acquisitions and sales based on the user's conditions and analysis results, proposes the best partners, and delivers the results to the user interface.

[1207] Step 18:

[1208] The user checks the proposed matching results on the terminal and proceeds with the transaction based on the proposed results.

[1209] Example 2

[1210] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1211] There is no system in place to ensure the efficient and effective execution of the M&A process. Furthermore, the lack of customization that takes user feelings into account during this process means that the user interface is not user-friendly and the user experience is not improved. Furthermore, various processes, such as verifying legal compliance of transaction contracts and providing advice on problem avoidance based on past cases, are performed manually, reducing efficiency and increasing the risk of errors.

[1212] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for providing a user interface for inputting information about the company and the target company; means for executing an artificial intelligence model to predict the company's performance after the acquisition based on the input information; means for displaying the prediction results on the user interface; means for executing an emotion engine that analyzes the user's emotions and transmits the data to the server; means for customizing the display content and proposal method based on the emotion data; means for automatically generating a transaction contract and performing legal checks based on the information and prediction results; means for generating advice to avoid problems by referring to past problem cases; and means for proposing optimal acquisition and sale matching. This makes each step of the M&A process more efficient, significantly reducing uncertainty, improving the user experience, and reducing legal risks and the probability of problems occurring.

[1213] A "user interface" is an input / output means for a user to input data and check results.

[1214] An "artificial intelligence model" is a program that uses machine learning algorithms to predict corporate performance.

[1215] An "emotion engine" is a software component that analyzes a user's emotions and provides that data.

[1216] "Validation" is the process of ensuring that entered data is accurate and appropriate.

[1217] A "database" is a system for systematically managing and retrieving stored information.

[1218] "Legal compliance" is the process of ensuring that contracts comply with relevant laws and regulations.

[1219] The "trouble case database" is a database that stores details of troubles that have occurred in the past.

[1220] "Customization" is the process of adjusting the functionality and appearance of a system based on a user's specific requirements and needs.

[1221] "Auto-generation" is the process by which the system automatically creates the necessary documents and information based on templates or existing data.

[1222] "Matching" is the process of finding and proposing companies that meet the conditions for acquisition or sale.

[1223] The present invention, "An integrated system for corporate acquisition and sale processes," is a system that efficiently and effectively supports M&A processes by combining multiple modules such as a user interface, an artificial intelligence model, an emotion engine, and a legal check module.

[1224] Key components of the system

[1225] 1. User Interface

[1226] Users use a terminal to input detailed data about their own company and the target company (e.g., financial data, business operations, number of employees, etc.). The interface is designed as an intuitive form and dashboard.

[1227] Example: A user enters the sales and profit margins of "Company A" into a form and selects "Company B" as the acquisition target.

[1228] 2. Database and Validation

[1229] The server receives the information entered by the user, performs validation to ensure the accuracy and integrity of the data, and if validation is successful, stores the information in the database.

[1230] Example: The server checks that the entered sales amount is a number and that the profit margin is within the range of 0-100%, and then saves it in the database.

[1231] 3. AI-based corporate performance prediction

[1232] The server sends the stored company information to a machine learning algorithm (e.g., TensorFlow, PyTorch) to predict how the company will perform after the acquisition. The artificial intelligence model predicts the company's profitability and growth potential.

[1233] Example: The server passes data on "Company A" and "Company B" to an AI module, which predicts a post-acquisition sales growth rate of 20% and a cost reduction effect of 15%.

[1234] 4. Displaying the prediction results

[1235] The server receives the prediction results from the AI ​​and sends them to the user's device in a format that is visually easy to understand, typically displayed as graphs or charts.

[1236] Example: The user checks the prediction results on the device and decides on the next action (e.g., further negotiation or contract generation).

[1237] 5. Emotion Recognition by Emotion Engine

[1238] As users enter information or check results, an emotion engine (e.g., OpenFace, Azure Emotion API) analyzes the user's facial expressions in real time through the camera.

[1239] Example: When a user is reviewing a prediction, the emotion engine detects that they are feeling anxious.

[1240] 6. Emotional customization

[1241] The server receives the emotion data and customizes the content and suggestions it displays based on that data, for example providing detailed explanations or additional information if the user is feeling anxious.

[1242] Example: A server displays FAQs and past success stories to a user who is feeling unsure.

[1243] 7. Automatic generation and legal check of transaction contracts

[1244] The user selects the automatic contract generation function based on the prediction results, and the server generates a customized contract based on the template contract, reflecting the user information and analysis results.

[1245] The server passes the generated contract to a legal checking module (e.g. LegalZoom API) to verify legal compliance.

[1246] Example: Checking that server-generated contracts contain the correct confidentiality clauses and payment terms.

[1247] 8. Providing advice to avoid problems

[1248] The server refers to a database of past trouble cases, searches for similar trouble cases, and generates advice.

[1249] Example: Based on the case where a payment delay occurs, the server provides advice on how to strictly set payment terms.

[1250] 9. Proposal of optimal matching

[1251] The server searches for and proposes the best buyer or seller based on the user's criteria and the results of AI analysis, and displays the proposals on the user interface.

[1252] Example: The server proposes that "Company C is the best acquisition target," and the user confirms the proposal and proceeds with the transaction.

[1253] Prompt Sentence Examples

[1254] 1. Example of company information: "Company A's sales revenue is 500 million yen, its profit margin is 10%, and it has 100 employees. I would like to select Company B as the acquisition target."

[1255] 2. Example of emotion engine analysis: "I'm feeling anxious. View detailed explanations and success stories."

[1256] By using this type of system configuration, each step of the M&A process can be made more efficient, and customization that takes user feelings into consideration and reduction of legal risks can be made possible.

[1257] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1258] Step 1: The user enters information about their company and the target company.

[1259] Input: Detailed company information, including financial data, business operations, and number of employees.

[1260] How it works: The user uses a terminal to enter detailed information about their own company and the target company into a special input form, such as sales, profit margin, number of employees, and business details.

[1261] Output: The entered company information is sent to the server.

[1262] Step 2: The server receives and validates the entered information.

[1263] Input: Company information entered by the user.

[1264] Behavior: The server validates the field format and values ​​of the received company information, such as whether sales are numeric and whether the profit margin is between 0% and 100%.

[1265] Output: If validation is successful, the correct company information is saved in the database for passing to the next processing step, otherwise an error message is generated and returned to the user.

[1266] Step 3: The server sends the company information to the machine learning algorithm.

[1267] Input: Validated company information.

[1268] How it works: The server retrieves validated company information from a database and sends it to a machine learning algorithm (e.g., TensorFlow, PyTorch).

[1269] Output: Company information sent to the machine learning algorithm.

[1270] Step 4: The AI ​​module predicts the company's performance.

[1271] Input: Company information sent from the server.

[1272] How it works: The AI ​​module uses machine learning algorithms to predict a company's profitability and growth potential, including sales growth, cost reductions, and profit margins.

[1273] Output: Predicted company performance data after acquisition (e.g., sales growth rate of 20%, cost reduction effect of 15%).

[1274] Step 5: The server formats the prediction results into a user interface and sends them.

[1275] Input: Prediction results obtained from the AI ​​module.

[1276] How it works: The server retrieves the prediction results and formats them into a format that is visually easy for the user to understand (e.g., graphs, charts).

[1277] Output: Send the formatted prediction results to the device's user interface.

[1278] Step 6: The user checks the prediction results.

[1279] Input: A formatted prediction result display.

[1280] How it works: The user uses a device to check the prediction results on the dashboard and decide on the next action (e.g., further negotiations, generating a contract).

[1281] Output: A decision for the user's next action.

[1282] Step 7: The emotion engine analyzes the user's emotions.

[1283] Input: User facial expressions and actions.

[1284] How it works: As users enter information or check prediction results, an emotion engine (e.g., OpenFace, Azure Emotion API) analyzes the user's facial expressions in real time via the camera.

[1285] Output: User emotion data.

[1286] Step 8: The server receives the emotion data and customizes the content and suggestions displayed.

[1287] Input: Emotion data from the emotion engine.

[1288] How it works: The server analyzes the emotion data and, if the user is feeling anxious, displays detailed explanations and additional information (e.g., success stories, FAQs) in the user interface.

[1289] Output: Customized display content and suggestions.

[1290] Step 9: The user selects the automatic contract generation feature.

[1291] Input: User decision.

[1292] Operation: The user uses the terminal to select the automatic contract generation function based on the generated prediction results.

[1293] Output: A request to auto-generate a contract.

[1294] Step 10: The server creates a customized contract based on the template contract.

[1295] Input: Auto-generated request and validated company information.

[1296] Operation: The server generates a customized contract based on the template contract, reflecting user information and the results of AI analysis.

[1297] Output: A customized contract.

[1298] Step 11: The legal check module analyzes the contract and checks for legal compliance.

[1299] Input: The generated customized agreement.

[1300] How it works: A legal check module (e.g. LegalZoom API) parses the generated contract and checks for legal requirements, such as the correct inclusion of confidentiality clauses and payment terms.

[1301] Output: A legally sound contract.

[1302] Step 12: The server refers to a database of past trouble cases and generates advice for avoiding the trouble.

[1303] Input: Contract information and trouble case database.

[1304] How it works: The server searches the database for past trouble cases and generates advice based on similar trouble cases.

[1305] Output: Advice information to help you avoid problems.

[1306] Step 13: The server performs optimal matching based on the user's requirements and the results of AI analysis.

[1307] Input: User conditions and AI analysis results.

[1308] Operation: The server searches the database for buyers and sellers that meet the user's criteria and performs matching.

[1309] Output: Best buy / sell proposal report.

[1310] Step 14: The user confirms the matching results and proceeds with the transaction.

[1311] Input: Matching proposal report.

[1312] How it works: The user reviews the proposed matches on their device and makes a decision to proceed with the specific transaction.

[1313] Output: Decision to enter a trade.

[1314] In this way, servers, terminals, and users can work together to carry out the M&A process efficiently and effectively.

[1315] (Application example 2)

[1316] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1317] In the traditional M&A process, systems existed that provided functions such as predicting post-acquisition corporate performance, automatically generating transaction contracts, and conducting legal checks. However, they lacked the means to recognize user emotions and improve the user experience in real time. Furthermore, systems that match appropriate acquisitions and sales deals or provide advice to avoid problems did not take user emotions into account, potentially increasing stress and anxiety and hindering appropriate decision-making. Therefore, in the M&A process, in addition to an efficient and effective system, there was a need for a system that could recognize user emotions in real time and customize support based on that information.

[1318] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1319] In this invention, the server includes: means for providing a user interface for inputting information about the company and the target company; means for executing an artificial intelligence model that predicts the company's performance after the acquisition based on the input information; means for displaying the prediction results on the user interface; means for executing an emotion recognition engine that analyzes emotion data input from the user interface; means for customizing the display content and proposal method of the user interface based on the emotion data; means for automatically generating a transaction contract and performing legal checks based on the information and prediction results; means for generating advice to avoid problems by referring to past trouble cases; and means for proposing optimal acquisition and sale matching. This makes each step of the M&A process more efficient, significantly reduces uncertainty, and alleviates user stress and anxiety, enabling appropriate decision-making.

[1320] A "user interface" is an interface through which a user inputs information and checks results.

[1321] An "artificial intelligence model" is a model that uses machine learning algorithms to analyze data and generate predictive results.

[1322] An "emotion recognition engine" is an engine that analyzes emotions in real time from data such as a user's facial expressions and voice.

[1323] A "commercial agreement" is a legal document that sets out the agreed-upon terms and conditions of a commercial transaction.

[1324] "Legal check" is the process of verifying whether a transaction contract meets legal requirements.

[1325] The "trouble case database" is a database that records details of past troubles and their solutions.

[1326] "Matching" is the process of proposing a buyer or seller based on specific criteria.

[1327] The following system and method are used as an embodiment of this invention: The system inputs information about the company and the target company, predicts the company's performance after the acquisition, performs sentiment analysis, automatically generates transaction contracts and performs legal checks, provides advice by referring to trouble cases, and proposes optimal matching.

[1328] The system consists of the following main modules:

[1329] 1. Providing a user interface

[1330] The user uses a terminal to input detailed data about their own company and the target company (financial data, business operations, number of employees, etc.). This information is sent to the server, validated, and then stored in a database. The user interface is built using web technologies such as HTML, CSS, and JavaScript.

[1331] 2. AI-based corporate performance prediction

[1332] The server sends the stored company information to an AI analysis module to predict the company's performance after the acquisition. The AI ​​module uses machine learning libraries such as TensorFlow and Keras to generate predictions such as sales growth rate, cost reduction effect, and profit margin. This makes it possible to predict the company's performance.

[1333] 3. Emotion analysis using an emotion recognition engine

[1334] While the user is entering information on the device or checking prediction results, the emotion recognition engine analyzes the user's facial expressions and input data. The emotion recognition engine uses OpenCV and TensorFlow to perform image processing and emotion analysis. For example, emotions such as anxiety, excitement, and relief are detected in real time and the data is sent to the server.

[1335] 4. Emotional customization

[1336] The server receives data from the emotion engine and customizes the content and suggestions displayed in the user interface. For example, if the user is feeling anxious, it can provide more detailed explanations or additional reassuring information, thereby reducing the user's stress and anxiety.

[1337] 5. Automatic generation and legal check of transaction contracts

[1338] The user selects the automatic contract generation function based on the prediction results. The server generates a customized contract based on the template contract, reflecting the user information and analysis results, and then checks legal requirements using a legal check module. The legal check module is composed of algorithms to meet various legal regulations and compliance requirements.

[1339] 6. Providing advice to avoid problems

[1340] The server refers to a database of past trouble cases and searches for similar trouble cases. Based on this, it generates advice for avoiding trouble and displays it on the user interface. For example, based on information that "in cases where problems occurred under similar conditions, there were delays in payment after the contract was signed," it provides advice on setting strict payment terms.

[1341] 7. Proposal of optimal matching

[1342] The server proposes optimal acquisition and sale matching based on the user's conditions and analysis results. The matching results are generated in report format and delivered to the user interface. The user can confirm this and proceed with the final transaction.

[1343] Specific examples

[1344] For example, in factory operations, when a user inputs production data and the number of employees, AI predicts production efficiency and cost reduction effects. Furthermore, an emotion recognition engine can detect employee dissatisfaction and display a message on the user interface saying, "Employee dissatisfaction detected. Action required." This allows appropriate action to be taken.

[1345] Prompt Sentence Examples

[1346] "We've detected employee dissatisfaction. Would you like to suggest improvements to maintain efficiency at 85%?"

[1347] "Employee satisfaction is high. Do you want to stick with the current plan?"

[1348] "The employee's status is neutral. Would you like to consider further improvements?"

[1349] In this way, the entire system can provide highly personalized support on the user's terms, making the M&A process efficient and effective.

[1350] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1351] Step 1:

[1352] The user uses a terminal to input detailed data such as financial data, business operations, and number of employees of their own company and the target company. The input information is sent to the server, where it is validated and stored in a database. The input data validation process checks the data format and checks for required fields.

[1353] Step 2:

[1354] The server sends data to an artificial intelligence model based on the stored company information to predict the company's performance after the acquisition. Specifically, it uses machine learning libraries such as TensorFlow and Keras to generate predictions such as sales growth rate, cost reduction effect, and profit margin. These prediction results are generated in JSON format and sent to the user interface.

[1355] Step 3:

[1356] The user checks the prediction results on the device. At this stage, the user's facial expressions and voice are collected through the camera and microphone and sent to the server. The emotion recognition engine analyzes this data using OpenCV and TensorFlow to detect emotions such as anxiety, excitement, and relief in real time. The analysis results are then sent to the server.

[1357] Step 4:

[1358] The server customizes the content and suggestions displayed in the user interface based on the emotion data from the emotion recognition engine. For example, if the user feels anxious, the server can display more detailed explanations and additional information to reassure the user. This can reduce the user's stress and anxiety.

[1359] Step 5:

[1360] After checking the prediction results, the user selects the automatic generation function for the transaction contract. The server generates a customized contract based on the template contract, reflecting the user information and analysis results. Furthermore, a legal check module is used to check legal requirements. This process includes an algorithm that uses natural language processing technology to check whether each clause in the contract meets legal requirements.

[1361] Step 6:

[1362] The server refers to a database of past trouble cases and searches for similar trouble cases. For example, based on information such as "in cases where problems occurred under similar conditions, there were delays in payments after the contract was signed," it generates advice for avoiding trouble. This advice is displayed on the user interface to help the user take appropriate action.

[1363] Step 7:

[1364] The server proposes optimal acquisition and sale matching based on the user's criteria and analysis results. The matching results are generated in report format and delivered to the user interface. The user can review this and proceed with the final transaction. The matching algorithm is designed to select the optimal partner based on input data and past success stories.

[1365] Through these steps, the entire system provides highly personalized support, streamlining the M&A process and reducing the psychological burden on users.

[1366] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1367] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1368] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1369] [Fourth embodiment]

[1370] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1371] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1372] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1373] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1374] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1375] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1376] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1377] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1378] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1379] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1380] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1381] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1382] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1383] MODE FOR CARRYING OUT THE INVENTION

[1384] The present invention, "Synergy Connect," is an integrated system for efficiently and effectively conducting corporate acquisition and sale (M&A) processes. The following describes the system's main functions and the specific program processing flow.

[1385] Key System Features

[1386] 1. Enter your company information

[1387] User: Uses a terminal to enter detailed data about their own company and the target company (financial data, business operations, number of employees, etc.).

[1388] Server: Receives the entered information, validates it, and saves it in the database.

[1389] 2. AI-based corporate performance prediction

[1390] Server: Passes saved company information to the AI ​​analysis module.

[1391] AI module: It uses machine learning algorithms to analyze data and predict post-acquisition company performance, specifically, sales growth and cost savings.

[1392] Server: Receives prediction results and converts them into a data format for display on the user interface.

[1393] 3. Displaying the prediction results

[1394] Server: Formats the analysis results and sends them to the terminal in a format that is easy for the user to understand.

[1395] User: Check the prediction results on the device and decide on the next action.

[1396] 4. Automatic generation and legal check of transaction contracts

[1397] User: If you want to proceed with a contract based on the prediction results, select the automatic contract generation function.

[1398] Server: Generates a customized contract based on the template contract, reflecting user information and predicted results. In addition, the server uses the legal check module to check legal requirements.

[1399] Legal Check Module: Analyzes contract content and performs legal compliance checks.

[1400] 5. Providing advice to avoid problems

[1401] Server: Refer to the database of past troubles and search for similar trouble cases.

[1402] Server: Generates advice to avoid problems and displays it in the user interface.

[1403] 6. Proposal of optimal matching

[1404] Server: Matches optimal acquisitions and sales based on user conditions and AI analysis results.

[1405] Server: Generates a report of the matching results and delivers it to the user interface.

[1406] User: Check the proposed matching results on the device and proceed with the transaction based on the suggestions.

[1407] Specific examples

[1408] Enter your company information

[1409] The user uses a terminal to input financial data (sales, profit margin, etc.) for "Company A" and selects "Company B" as the acquisition target. The server stores this information in a database and performs validation.

[1410] AI-based business performance prediction

[1411] The server passes data from "Company A" and "Company B" to the AI ​​module, which predicts a 20% sales growth rate and a 15% cost reduction for "Company A + B" after the acquisition. The server formats the results and sends them to the user interface.

[1412] Automatic generation and legal check of trade contracts

[1413] The user checks the prediction results and selects the automatic contract generation function. The server customizes the contract based on a template and performs legal checks, such as checking that confidentiality clauses and payment terms are included correctly.

[1414] Providing advice to avoid problems

[1415] The server refers to a database of past problems and provides advice on setting strict payment terms based on information such as "cases where problems occurred under similar conditions resulted in payment delays after the contract was signed."

[1416] Proposal of optimal matching

[1417] The server proposes "Company C" as the most suitable acquisition target based on the user's conditions and the results of the AI ​​analysis. The results are displayed on the user interface, and the user confirms the proposal before proceeding with the final transaction.

[1418] It is expected that the invention, "Synergy Connect," will streamline each step of the M&A process and significantly reduce uncertainty.

[1419] The processing flow will be explained below.

[1420] Step 1:

[1421] Users access the Synergy Connect platform through their devices and enter information about their company and the target company, including details such as financial data, business operations, number of employees, and sales.

[1422] Step 2:

[1423] The server receives the information entered by the user and validates the input data. Validation checks include checking required fields and ensuring the correctness of the data format. If validation is successful, the information is saved in the database.

[1424] Step 3:

[1425] The server sends the stored company information to an artificial intelligence module, which uses machine learning algorithms to analyze the input data and predicts the company's performance after the acquisition.

[1426] Step 4:

[1427] An artificial intelligence module preprocesses the received data and converts it into a format suitable for analysis, which includes standardizing the data and imputing missing values.

[1428] Step 5:

[1429] The AI ​​module uses the pre-processed data to predict the company's performance after the acquisition, calculating, for example, sales growth rate, cost reduction effect, profit margin, etc. It generates the prediction results and returns them to the server.

[1430] Step 6:

[1431] The server receives the prediction results returned by the AI ​​module and converts them into a data format for display on the user interface. The results are formatted in a simple and intuitive format.

[1432] Step 7:

[1433] The server then sends the formatted prediction results to the user's device, where detailed analysis results, such as synergy effects and risk analysis, are displayed.

[1434] Step 8:

[1435] The user can review the prediction results on their device and decide on the next action, for example, to automatically generate a contract.

[1436] Step 9:

[1437] The server executes the automatic generation function of the transaction contract based on the user's request, and creates a customized contract based on the template contract, reflecting the user information and analysis results.

[1438] Step 10:

[1439] The server sends the automatically generated contract to the legal check module to check for legal compliance.

[1440] Step 11:

[1441] The legal check module analyzes the contents of the contract, checks for necessary legal requirements and compliance, and returns the check results to the server.

[1442] Step 12:

[1443] The server receives the results of the legal checks, modifies the contract as needed, and then sends the finalized contract to the user interface.

[1444] Step 13:

[1445] The server refers to a database of past problems to search for similar cases, generates advice for avoiding the problem, and sends it to the user.

[1446] Step 14:

[1447] The user checks advice on how to avoid problems on the device and takes the necessary measures.

[1448] Step 15:

[1449] The server performs optimal matching for acquisitions and sales based on the user's conditions and analysis results, proposes the best partners, and delivers the results to the user interface.

[1450] Step 16:

[1451] The user checks the proposed matching results on the terminal and proceeds with the transaction based on the proposed results.

[1452] Example 1

[1453] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1454] Traditional corporate acquisition and sale processes are complex and time-consuming, requiring the management of vast amounts of information, forecasting future corporate performance, and legal procedures. In particular, the creation of transaction contracts and legal checks, as well as the data analysis required for acquisition matching, require advanced expertise, placing a significant burden on companies. For this reason, a system was needed to streamline the entire acquisition process and ensure it proceeds accurately and quickly.

[1455] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1456] In this invention, the server includes means for providing a user screen for inputting information about the company and the target company, means for executing a machine learning model to predict post-acquisition corporate performance based on the input information, means for displaying the prediction results on the user screen, means for automatically generating transaction documents and conducting legal confirmation based on the information and prediction results, means for referencing a database of past cases to generate advice for problem avoidance, and means for making optimal acquisition and sale proposals. This makes it possible to efficiently carry out everything from data input in the acquisition and sale process to predictions, contract generation, legal confirmation, problem avoidance, and matching proposals all at once.

[1457] "Information about your company and the target company" refers to detailed data necessary for acquisitions and sales, such as the company's financial data, business operations, and number of employees.

[1458] The "user screen" is the interface through which users input information about their own company and the target company via a terminal and receive feedback from the system.

[1459] A "machine learning model" is an artificial intelligence system that contains algorithms for analyzing data and predicting a company's performance after an acquisition.

[1460] "Transaction Documents" means documents, including documents required for acquisition and sale agreements, that require automated generation and legal review.

[1461] "Legal Review" refers to the process of verifying that transaction documents are legally sound and compliant.

[1462] A "case database" is a database that records cases of troubles and problems that have occurred in the past.

[1463] "Advice" refers to suggestions for avoiding problems that are generated based on a database of past cases.

[1464] "Buy / Sell Proposal" is the process of recommending the most suitable trading partner based on the user's criteria and analysis results.

[1465] The present invention is an integrated system for efficiently managing the process of acquiring and selling a company. This system functions mainly through users, terminals, and servers. Each element and its specific function will be described below.

[1466] Enter your company information

[1467] User: Enters information about their own company and the target company using a terminal. For example, they enter financial data for "Company A" (sales of 50 million yen, profit margin of 10%, etc.), and selects "Company B" as the acquisition target.

[1468] Terminal: Generates an HTTP POST request to send the entered data to the server.

[1469] Server: Receives the input information, validates the data, and stores the successfully validated data in a database (e.g. MySQL).

[1470] AI-based business performance prediction

[1471] Server: Converts stored corporate data into the required format to be passed to the AI ​​analysis module.

[1472] AI module: Uses machine learning technology to predict the company's performance after the acquisition. For example, it calculates a sales growth rate of 20% and a cost reduction effect of 15%. This uses a general-purpose machine learning framework (e.g., TensorFlow).

[1473] Server: Receives prediction results and converts the data into a format that can be displayed in the user interface.

[1474] Displaying prediction results

[1475] Server: Formats the prediction results and generates an HTTP response to send to the user interface.

[1476] Terminal: The received forecast result data is displayed on the user interface. For example, "The forecasted sales growth rate is 20%."

[1477] Automatic generation and legal check of trade contracts

[1478] User: Check the prediction results and select the automatic contract generation function to proceed with the transaction agreement.

[1479] Server: Uses a contract template to generate a customized contract that reflects the user's information and predicted results. This is done using a template management tool (e.g., a general API service).

[1480] Legal Check Module: Ensures that generated contracts are legally sound and that confidentiality clauses and payment terms are included correctly.

[1481] Providing advice to avoid problems

[1482] Server: Refer to the database of past problems to search for similar cases. The database used is (e.g., MySQL).

[1483] Server: Based on the search results, generate advice to avoid problems. For example, provide advice such as "We recommend that you set strict payment terms."

[1484] Proposal of optimal matching

[1485] Server: Based on the user's requirements and the results of AI analysis, the server makes optimal acquisition and sale proposals. This process uses a recommendation engine (e.g., AWS SageMaker).

[1486] Server: Generates a report of the proposal results and delivers it to the user interface.

[1487] Terminal: Based on the received proposal results, it displays them on the user interface and the user decides whether to proceed with the transaction.

[1488] Prompt Sentence Examples

[1489] "Enter Company A's financial data (sales: 50 million yen, profit margin: 10%) into the system and select Company B as the acquisition target."

[1490] "Using data from companies A and B, the system predicted a 20% sales growth rate and a 15% cost reduction."

[1491] "Based on the prediction results, generate customized contracts and run legal checks, for example, to ensure confidentiality clauses and payment terms are included correctly."

[1492] "We will refer to our database of past troubles and provide advice on how to prevent payment delays. We recommend that you set strict payment terms."

[1493] "Based on the results of the system analysis, we propose Company C as the most suitable acquisition target. Please review this proposal and proceed with the transaction."

[1494] The system streamlines the process of acquiring and selling companies, making it faster, more accurate, and more efficient.

[1495] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1496] Step 1: The user starts the terminal and logs in to the Synergy Connect system. The user enters the login information (username, password) and the terminal sends it to the server. The server performs the authentication process and displays the dashboard if the login is successful.

[1497] Step 2: The user moves to the company information input screen. The terminal displays an input form for the user to enter. The user enters detailed data about their own company and the target company (e.g., sales of 50 million yen, profit margin of 10%). The terminal sends the entered data to the server as an HTTP POST request.

[1498] Step 3: The server passes the received company information to the validation module to check whether the data format and required fields are correct. If the input data is correct, the server saves it in a database (e.g. MySQL). If the validation fails, it returns an error message to the terminal.

[1499] Step 4: The server retrieves information about Company A and Company B from the database and passes it to the AI ​​analysis module. The AI ​​module uses TensorFlow to analyze the data and predict the company's performance after the acquisition. The input data is the company's financial and performance data, and the output is predicted values ​​such as sales growth rate and cost reduction effect.

[1500] Step 5: The AI ​​module generates a prediction result. For example, it calculates a sales growth rate of 20% or a cost reduction effect of 15%. The server receives the prediction result and converts it into a format (e.g., JSON) that can be displayed in the user interface.

[1501] Step 6: The server sends the converted prediction results to the terminal as an HTTP response. The terminal analyzes the received data and displays the prediction results on a dashboard or report screen. The user confirms information such as "The predicted sales growth rate is 20%."

[1502] Step 7: The user checks the prediction results and selects the automatic contract generation function to proceed with the transaction contract. The terminal sends this request to the server. The server generates a customized contract based on the contract template, reflecting the user information and prediction results. It uses a template management tool (e.g., a general API service).

[1503] Step 8: The server passes the generated contract to the legal check module to check compliance. The legal check module analyzes whether confidentiality clauses and payment terms are included. The analysis result is returned to the server as feedback such as "The contract is legally valid."

[1504] Step 9: The server searches a database of past incidents to find similar incidents. This process uses a MySQL database. The input for the database search is contract terms and company information, and the output is similar incidents and their solutions.

[1505] Step 10: The server generates advice to avoid problems based on the search results. For example, advice such as "We recommend that you set strict payment terms" is generated and sent to the terminal. The user can then adjust the contract terms by checking this advice.

[1506] Step 11: The server makes optimal acquisition and sale proposals based on the user's conditions and the results of AI analysis. It uses a recommendation engine (e.g., AWS SageMaker) to recommend the best trading partner.

[1507] Step 12: The server generates a report of the proposal results and sends it to the terminal as an HTTP response. The terminal analyzes the proposal contents and displays them on the user interface. The user confirms information such as "Company C has been proposed as the optimal acquisition target" and decides whether to proceed with the transaction.

[1508] (Application example 1)

[1509] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1510] Currently, there is a lack of systems for efficiently inputting and analyzing vehicle usage data and proposing appropriate routes when it comes to operating and managing autonomous vehicles. In particular, there is a lack of integration between elements such as vehicle performance prediction, generation of transaction contracts, legal checks, and advice on trouble avoidance, which entails a great deal of effort and risk. This has led to issues such as increased operating costs and inadequate risk management.

[1511] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1512] In this invention, the server includes means for providing a user interface for inputting information about the company and the target company, means for executing an artificial intelligence model that predicts the company's performance after the acquisition based on the input information, means for displaying the prediction results on the user interface, means for automatically generating a transaction contract and performing legal checks based on the information and the prediction results, means for referring to past trouble cases and generating advice for avoiding trouble, means for proposing optimal acquisition and sale matching, means for inputting autonomous vehicle usage data, means for predicting vehicle performance based on the usage data, and means for optimizing operation routes based on the performance prediction results.This enables an integrated system that can efficiently perform operations and management of autonomous vehicles, from data input to analysis, contract generation, trouble avoidance, and optimized operation route proposals.

[1513] A "user interface" is a screen or operating means that allows a user to input information or check the results of the system.

[1514] An "artificial intelligence model" is software that includes machine learning algorithms to make predictions and analyses based on collected data.

[1515] "Prediction results" are information indicating future performance or outcomes obtained from data analyzed by an artificial intelligence model.

[1516] A "transaction contract" is a document that describes the contents of the contract and clarifies the legal matters related to the transaction.

[1517] "Legal check" is the process of verifying that the generated transaction contract is legally sound.

[1518] The "trouble case database" is a database that collects information about troubles that have occurred in the past and the countermeasures that were taken to deal with them.

[1519] "Advice for avoiding trouble" is based on past trouble cases and provides suggestions and measures to avoid similar problems.

[1520] "Acquisition and sale matching" is the process of making proposals and recommendations to find suitable acquisition or sale targets.

[1521] "Autonomous vehicle usage data" means data about vehicle performance, such as vehicle usage hours, maintenance costs, and driving records.

[1522] "Vehicle performance" is a comprehensive performance indicator of an autonomous vehicle, including its operating efficiency, energy consumption, and maintenance status.

[1523] "Route optimization" is the process of calculating and proposing routes for autonomous vehicles to reach their destinations with the greatest efficiency.

[1524] MODE FOR CARRYING OUT THE INVENTION

[1525] The present invention, "AutoDrive Synergy Manager," is an integrated system for streamlining the operation and management of autonomous vehicles. This system has a variety of functions for data entry, analysis, prediction, contract generation, legal checks, trouble avoidance, and route optimization.

[1526] Hardware and software used

[1527] Hardware

[1528] Smartphone

[1529] server

[1530] Autonomous vehicles

[1531] software

[1532] Python: Various data processing and AI model execution

[1533] Pandas: Working with Data Frames

[1534] NumPy: Numerical Computation

[1535] scikit-learn: machine learning algorithms

[1536] python-docx: Generate contracts

[1537] System Overview

[1538] 1. User Interface

[1539] The server provides a user interface for entering autonomous vehicle usage data via a smartphone, allowing users to enter detailed information such as vehicle usage hours, maintenance costs, and driving records.

[1540] 2. Data Validation

[1541] The data entered from the smartphone is sent to the server and validated to ensure data integrity and to check for inaccuracies.

[1542] 3. Vehicle performance prediction

[1543] The server then passes the validated data to an artificial intelligence model (e.g., Linear Regression) to predict vehicle performance, including operational efficiency, energy consumption, and maintenance status.

[1544] 4. Displaying the prediction results

[1545] The predicted performance data is formatted and displayed in a user interface, allowing the user to intuitively understand the vehicle's condition.

[1546] 5. Automatic generation and legal check of transaction contracts

[1547] The server automatically generates a transaction contract based on the prediction results, and the generated contract undergoes legal checks to confirm legal compliance.

[1548] 6. Advice for avoiding trouble

[1549] The server refers to a database of past trouble cases and generates advice for avoiding troubles that have occurred under similar conditions.

[1550] 7. Route optimization

[1551] The server calculates and proposes optimal routes based on the predicted vehicle performance, thereby maximizing operational efficiency.

[1552] Specific examples

[1553] Enter company information and predict performance

[1554] For example, if data is entered indicating that the vehicle ID "V001" has been in use for 1,000 hours and that the maintenance cost is 2,000 yen, the server will predict vehicle performance based on this data. The result will be displayed as "Predicted vehicle performance score is 85."

[1555] Generate a trade agreement

[1556] Based on the prediction results, the automatic contract generation function generates a document titled "Contract Terms: Sample Terms" and performs legal checks. This contract can be viewed on the user's smartphone.

[1557] Prompt Sentence Examples

[1558] Enter the following data: "Vehicle ID: V001, Usage time: 1000 hours, Maintenance cost: 2000 yen" and let me know the predicted performance of this vehicle. Also, please automatically generate a contract.

[1559] Each step of the invention is expected to improve the efficiency of autonomous vehicle operation and management, significantly reducing operational costs and improving risk management.

[1560] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1561] Step 1:

[1562] A user uses a smartphone to input usage data for an autonomous vehicle (e.g., vehicle ID, usage time, maintenance cost) and sends it to the server. The input data includes the vehicle ID "V001," usage time "1000 hours," and maintenance cost "2000 yen." This data becomes the input information.

[1563] Step 2:

[1564] The server receives the entered data and performs data validation. Data validation checks the consistency and completeness of the entered data and eliminates inaccurate information. For example, it checks whether the usage time is a negative number or whether the maintenance cost is within a realistic range. This ensures the reliability of the data.

[1565] Step 3:

[1566] The server passes the data that has passed validation to an artificial intelligence model (Linear Regression) for analysis. Specifically, it predicts the vehicle performance score using usage time and maintenance costs as input data. The prediction model has learned from past vehicle usage data and outputs a performance score of "85" based on the input data.

[1567] Step 4:

[1568] The server displays the predicted performance score on the user interface. The user can then check the results via their smartphone. Specifically, the server displays the predicted performance score for vehicle ID: V001 as 85.

[1569] Step 5:

[1570] If the user selects automatic contract generation, the server generates a transaction contract based on the prediction results. First, the template contract is updated with the vehicle ID and predicted performance score. Next, a legal compliance check is performed to ensure, for example, that confidentiality clauses and payment terms are properly included. Finally, the generated contract is provided in a format that can be downloaded to a smartphone.

[1571] Step 6:

[1572] The server references a database of past trouble cases and generates advice for avoiding trouble under similar conditions. For example, advice such as "In a case where a problem occurred under similar conditions, there was a payment delay, so you should set strict payment terms" is provided. The results are displayed on the user interface.

[1573] Step 7:

[1574] The server calculates and proposes the optimal route based on the results of vehicle performance predictions. It calculates parameters such as usage time, maintenance costs, and operational efficiency as input data, and outputs the optimal route information. Users can check the proposed route on their smartphone and create a driving plan.

[1575] This will enable more efficient operation and management of autonomous vehicles, reducing operational costs and improving risk management.

[1576] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1577] MODE FOR CARRYING OUT THE INVENTION

[1578] The present invention, "Synergy Connect with Emotional Engine," is an integrated system for efficient and effective M&A processes, and further enhances the user experience by combining it with an emotional engine that recognizes the user's emotions.

[1579] Key System Features

[1580] 1. Enter your company information

[1581] User: Uses a terminal to enter detailed data about their own company and the target company (financial data, business operations, number of employees, etc.).

[1582] Server: Receives the entered information, validates it, and saves it in the database.

[1583] 2. AI-based corporate performance prediction

[1584] Server: Sends the stored company information to the AI ​​analysis module, which uses machine learning algorithms to analyze the data and predicts the company's performance after acquisition.

[1585] AI module: Uses machine learning algorithms to analyze data and generate predictions, such as sales growth, cost reduction, and profit margins.

[1586] Server: Receives prediction results and converts them into a data format for display on the user interface.

[1587] 3. Displaying the prediction results

[1588] Server: Formats the analysis results and sends them to the terminal in a format that is easy for the user to understand.

[1589] User: Check the prediction results on the device and decide on the next action.

[1590] 4. Emotion Recognition by Emotion Engine

[1591] User: When entering information or viewing results, the emotion engine analyzes the user's facial expressions and input data to determine their emotions, such as anxiety, excitement, or relief.

[1592] Emotion engine: Analyzes the user's emotions in real time and sends the emotional data to the server, thereby detecting the user's stress and anxiety.

[1593] 5. Emotional customization

[1594] Server: Receives emotion data from the emotion engine and customizes what is displayed and how suggestions are made, for example, if the user is feeling anxious, providing a more detailed explanation or additional information to reassure them.

[1595] 6. Automatic generation and legal check of transaction contracts

[1596] User: Select the automatic contract generation function based on the prediction results.

[1597] Server: Generates a customized contract based on the template contract, reflecting user information and analysis results. In addition, the legal check module checks for legal requirements.

[1598] Legal Check Module: Analyzes contract content and performs legal compliance checks.

[1599] 7. Providing advice to avoid problems

[1600] Server: Refer to the database of past problems and search for similar cases.

[1601] Server: Generates advice to avoid problems and displays it in the user interface.

[1602] 8. Proposal of optimal matching

[1603] Server: Matches optimal acquisitions and sales based on user conditions and analysis results.

[1604] Server: Generates a report of the matching results and delivers it to the user interface.

[1605] User: Check the proposed matching results on the device and proceed with the transaction based on the suggestions.

[1606] Specific examples

[1607] Enter your company information

[1608] The user uses a terminal to input financial data (sales, profit margin, etc.) for "Company A" and selects "Company B" as the acquisition target. The server stores this information in a database and performs validation.

[1609] AI-based business performance prediction

[1610] The server passes data from "Company A" and "Company B" to the AI ​​module, which predicts a 20% sales growth rate and a 15% cost reduction for "Company A+B" after the acquisition. The server formats the results and sends them to the user interface.

[1611] Emotion recognition and customization with emotion engine

[1612] When the user confirms the prediction, the emotion engine analyzes the user's facial expression and detects anxiety. The server receives this information and can, for example, provide more detailed explanations or follow-up questions to alleviate the user's anxiety.

[1613] Automatic generation and legal check of trade contracts

[1614] The user checks the prediction results and selects the automatic contract generation function. The server customizes the contract based on a template and performs legal checks, such as checking that confidentiality clauses and payment terms are included correctly.

[1615] Providing advice to avoid problems

[1616] The server refers to a database of past problems and provides advice on setting strict payment terms based on information such as "cases where problems occurred under similar conditions resulted in payment delays after the contract was signed."

[1617] Proposal of optimal matching

[1618] The server proposes "Company C" as the most suitable acquisition target based on the user's conditions and the results of the AI ​​analysis. The results are displayed on the user interface, and the user confirms the proposal before proceeding with the final transaction.

[1619] Our invention, "Synergy Connect with Emotional Engine," streamlines each step of the M&A process, significantly reduces uncertainty, and improves the user experience.

[1620] The processing flow will be explained below.

[1621] Step 1:

[1622] Users access the Synergy Connect platform through their devices and enter information about their company and the target company, including details such as financial data, business operations, number of employees, and sales.

[1623] Step 2:

[1624] The server receives the information entered by the user and validates the input data. Validation checks include checking required fields and ensuring the correctness of the data format. If validation is successful, the information is saved in the database.

[1625] Step 3:

[1626] The server sends the stored company information to an artificial intelligence module, which uses machine learning algorithms to analyze the data and predicts the company's performance after the acquisition.

[1627] Step 4:

[1628] An artificial intelligence module preprocesses the received data and converts it into a format suitable for analysis, which includes standardizing the data and imputing missing values.

[1629] Step 5:

[1630] The AI ​​module uses the pre-processed data to predict the company's performance after the acquisition, such as calculating sales growth rate, cost reduction effect, profit margin, etc. It generates the prediction results and returns them to the server.

[1631] Step 6:

[1632] The server receives the prediction results returned by the AI ​​module and converts them into a data format for display on the user interface. The results are formatted in a simple and intuitive format.

[1633] Step 7:

[1634] The server then sends the formatted prediction results to the user's device, where detailed analysis results, such as synergy effects and risk analysis, are displayed.

[1635] Step 8:

[1636] While the user is checking the prediction results on the device, the emotion engine analyzes the user's facial expressions and input patterns to estimate their emotional state (e.g., anxiety, excitement, relief, etc.) in real time.

[1637] Step 9:

[1638] The emotion engine analyzes the emotion data and sends it to the server. The server then customizes the content and suggestions it displays based on the emotion data it receives. For example, if the user is feeling anxious, the server will display more detailed explanations and additional support information.

[1639] Step 10:

[1640] Based on the prediction results, the user selects the automatic contract generation function.

[1641] Step 11:

[1642] The server automatically generates a transaction contract based on the user's request. Based on the template contract, it creates a customized contract that reflects the user information and analysis results.

[1643] Step 12:

[1644] The server sends the automatically generated contract to the legal check module to check for legal compliance.

[1645] Step 13:

[1646] The legal check module analyzes the contents of the contract, checks for necessary legal requirements and compliance, and returns the check results to the server.

[1647] Step 14:

[1648] The server receives the results of the legal checks, modifies the contract as needed, and then sends the finalized contract to the user interface.

[1649] Step 15:

[1650] The server references a database of past troubles to search for similar trouble cases, generates advice for avoiding the trouble, and sends it to the user's device.

[1651] Step 16:

[1652] The user checks advice on how to avoid problems on the device and takes the necessary measures.

[1653] Step 17:

[1654] The server performs optimal matching for acquisitions and sales based on the user's conditions and analysis results, proposes the best partners, and delivers the results to the user interface.

[1655] Step 18:

[1656] The user checks the proposed matching results on the terminal and proceeds with the transaction based on the proposed results.

[1657] Example 2

[1658] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1659] There is no system in place to ensure the efficient and effective execution of the M&A process. Furthermore, the lack of customization that takes user feelings into account during this process means that the user interface is not user-friendly and the user experience is not improved. Furthermore, various processes, such as verifying legal compliance of transaction contracts and providing advice on problem avoidance based on past cases, are performed manually, reducing efficiency and increasing the risk of errors.

[1660] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for providing a user interface for inputting information about the company and the target company; means for executing an artificial intelligence model to predict the company's performance after the acquisition based on the input information; means for displaying the prediction results on the user interface; means for executing an emotion engine that analyzes the user's emotions and transmits the data to the server; means for customizing the display content and proposal method based on the emotion data; means for automatically generating a transaction contract and performing legal checks based on the information and prediction results; means for generating advice to avoid problems by referring to past problem cases; and means for proposing optimal acquisition and sale matching. This makes each step of the M&A process more efficient, significantly reducing uncertainty, improving the user experience, and reducing legal risks and the probability of problems occurring.

[1661] A "user interface" is an input / output means for a user to input data and check results.

[1662] An "artificial intelligence model" is a program that uses machine learning algorithms to predict corporate performance.

[1663] An "emotion engine" is a software component that analyzes a user's emotions and provides that data.

[1664] "Validation" is the process of ensuring that entered data is accurate and appropriate.

[1665] A "database" is a system for systematically managing and retrieving stored information.

[1666] "Legal compliance" is the process of ensuring that contracts comply with relevant laws and regulations.

[1667] The "trouble case database" is a database that stores details of troubles that have occurred in the past.

[1668] "Customization" is the process of adjusting the functionality and appearance of a system based on a user's specific requirements and needs.

[1669] "Auto-generation" is the process by which the system automatically creates the necessary documents and information based on templates or existing data.

[1670] "Matching" is the process of finding and proposing companies that meet the conditions for acquisition or sale.

[1671] The present invention, "An integrated system for corporate acquisition and sale processes," is a system that efficiently and effectively supports M&A processes by combining multiple modules such as a user interface, an artificial intelligence model, an emotion engine, and a legal check module.

[1672] Key components of the system

[1673] 1. User Interface

[1674] Users use a terminal to input detailed data about their own company and the target company (e.g., financial data, business operations, number of employees, etc.). The interface is designed as an intuitive form and dashboard.

[1675] Example: A user enters the sales and profit margins of "Company A" into a form and selects "Company B" as the acquisition target.

[1676] 2. Database and Validation

[1677] The server receives the information entered by the user, performs validation to ensure the accuracy and integrity of the data, and if validation is successful, stores the information in the database.

[1678] Example: The server checks that the entered sales amount is a number and that the profit margin is within the range of 0-100%, and then saves it in the database.

[1679] 3. AI-based corporate performance prediction

[1680] The server sends the stored company information to a machine learning algorithm (e.g., TensorFlow, PyTorch) to predict how the company will perform after the acquisition. The artificial intelligence model predicts the company's profitability and growth potential.

[1681] Example: The server passes data on "Company A" and "Company B" to an AI module, which predicts a post-acquisition sales growth rate of 20% and a cost reduction effect of 15%.

[1682] 4. Displaying the prediction results

[1683] The server receives the prediction results from the AI ​​and sends them to the user's device in a format that is visually easy to understand, typically displayed as graphs or charts.

[1684] Example: The user checks the prediction results on the device and decides on the next action (e.g., further negotiation or contract generation).

[1685] 5. Emotion Recognition by Emotion Engine

[1686] As users enter information or check results, an emotion engine (e.g., OpenFace, Azure Emotion API) analyzes the user's facial expressions in real time through the camera.

[1687] Example: When a user is reviewing a prediction, the emotion engine detects that they are feeling anxious.

[1688] 6. Emotional customization

[1689] The server receives the emotion data and customizes the content and suggestions it displays based on that data, for example providing detailed explanations or additional information if the user is feeling anxious.

[1690] Example: A server displays FAQs and past success stories to a user who is feeling unsure.

[1691] 7. Automatic generation and legal check of transaction contracts

[1692] The user selects the automatic contract generation function based on the prediction results, and the server generates a customized contract based on the template contract, reflecting the user information and analysis results.

[1693] The server passes the generated contract to a legal checking module (e.g. LegalZoom API) to verify legal compliance.

[1694] Example: Checking that server-generated contracts contain the correct confidentiality clauses and payment terms.

[1695] 8. Providing advice to avoid problems

[1696] The server refers to a database of past trouble cases, searches for similar trouble cases, and generates advice.

[1697] Example: Based on the case where a payment delay occurs, the server provides advice on how to strictly set payment terms.

[1698] 9. Proposal of optimal matching

[1699] The server searches for and proposes the best buyer or seller based on the user's criteria and the results of AI analysis, and displays the proposals on the user interface.

[1700] Example: The server proposes that "Company C is the best acquisition target," and the user confirms the proposal and proceeds with the transaction.

[1701] Prompt Sentence Examples

[1702] 1. Example of company information: "Company A's sales revenue is 500 million yen, its profit margin is 10%, and it has 100 employees. I would like to select Company B as the acquisition target."

[1703] 2. Example of emotion engine analysis: "I'm feeling anxious. View detailed explanations and success stories."

[1704] By using this type of system configuration, each step of the M&A process can be made more efficient, and customization that takes user feelings into consideration and reduction of legal risks can be made possible.

[1705] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1706] Step 1: The user enters information about their company and the target company.

[1707] Input: Detailed company information, including financial data, business operations, and number of employees.

[1708] How it works: The user uses a terminal to enter detailed information about their own company and the target company into a special input form, such as sales, profit margin, number of employees, and business details.

[1709] Output: The entered company information is sent to the server.

[1710] Step 2: The server receives and validates the entered information.

[1711] Input: Company information entered by the user.

[1712] Behavior: The server validates the field format and values ​​of the received company information, such as whether sales are numeric and whether the profit margin is between 0% and 100%.

[1713] Output: If validation is successful, the correct company information is saved in the database for passing to the next processing step, otherwise an error message is generated and returned to the user.

[1714] Step 3: The server sends the company information to the machine learning algorithm.

[1715] Input: Validated company information.

[1716] How it works: The server retrieves validated company information from a database and sends it to a machine learning algorithm (e.g., TensorFlow, PyTorch).

[1717] Output: Company information sent to the machine learning algorithm.

[1718] Step 4: The AI ​​module predicts the company's performance.

[1719] Input: Company information sent from the server.

[1720] How it works: The AI ​​module uses machine learning algorithms to predict a company's profitability and growth potential, including sales growth, cost reductions, and profit margins.

[1721] Output: Predicted company performance data after acquisition (e.g., sales growth rate of 20%, cost reduction effect of 15%).

[1722] Step 5: The server formats the prediction results into a user interface and sends them.

[1723] Input: Prediction results obtained from the AI ​​module.

[1724] How it works: The server retrieves the prediction results and formats them into a format that is visually easy for the user to understand (e.g., graphs, charts).

[1725] Output: Send the formatted prediction results to the device's user interface.

[1726] Step 6: The user checks the prediction results.

[1727] Input: A formatted prediction result display.

[1728] How it works: The user uses a device to check the prediction results on the dashboard and decide on the next action (e.g., further negotiations, generating a contract).

[1729] Output: A decision for the user's next action.

[1730] Step 7: The emotion engine analyzes the user's emotions.

[1731] Input: User facial expressions and actions.

[1732] How it works: As users enter information or check prediction results, an emotion engine (e.g., OpenFace, Azure Emotion API) analyzes the user's facial expressions in real time via the camera.

[1733] Output: User emotion data.

[1734] Step 8: The server receives the emotion data and customizes the content and suggestions displayed.

[1735] Input: Emotion data from the emotion engine.

[1736] How it works: The server analyzes the emotion data and, if the user is feeling anxious, displays detailed explanations and additional information (e.g., success stories, FAQs) in the user interface.

[1737] Output: Customized display content and suggestions.

[1738] Step 9: The user selects the automatic contract generation feature.

[1739] Input: User decision.

[1740] Operation: The user uses the terminal to select the automatic contract generation function based on the generated prediction results.

[1741] Output: A request to auto-generate a contract.

[1742] Step 10: The server creates a customized contract based on the template contract.

[1743] Input: Auto-generated request and validated company information.

[1744] Operation: The server generates a customized contract based on the template contract, reflecting user information and the results of AI analysis.

[1745] Output: A customized contract.

[1746] Step 11: The legal check module analyzes the contract and checks for legal compliance.

[1747] Input: The generated customized agreement.

[1748] How it works: A legal check module (e.g. LegalZoom API) parses the generated contract and checks for legal requirements, such as the correct inclusion of confidentiality clauses and payment terms.

[1749] Output: A legally sound contract.

[1750] Step 12: The server refers to a database of past trouble cases and generates advice for avoiding the trouble.

[1751] Input: Contract information and trouble case database.

[1752] How it works: The server searches the database for past trouble cases and generates advice based on similar trouble cases.

[1753] Output: Advice information to help you avoid problems.

[1754] Step 13: The server performs optimal matching based on the user's requirements and the results of AI analysis.

[1755] Input: User conditions and AI analysis results.

[1756] Operation: The server searches the database for buyers and sellers that meet the user's criteria and performs matching.

[1757] Output: Best buy / sell proposal report.

[1758] Step 14: The user confirms the matching results and proceeds with the transaction.

[1759] Input: Matching proposal report.

[1760] How it works: The user reviews the proposed matches on their device and makes a decision to proceed with the specific transaction.

[1761] Output: Decision to enter a trade.

[1762] In this way, servers, terminals, and users can work together to carry out the M&A process efficiently and effectively.

[1763] (Application example 2)

[1764] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1765] In the traditional M&A process, systems existed that provided functions such as predicting post-acquisition corporate performance, automatically generating transaction contracts, and conducting legal checks. However, they lacked the means to recognize user emotions and improve the user experience in real time. Furthermore, systems that match appropriate acquisitions and sales deals or provide advice to avoid problems did not take user emotions into account, potentially increasing stress and anxiety and hindering appropriate decision-making. Therefore, in the M&A process, in addition to an efficient and effective system, there was a need for a system that could recognize user emotions in real time and customize support based on that information.

[1766] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1767] In this invention, the server includes: means for providing a user interface for inputting information about the company and the target company; means for executing an artificial intelligence model that predicts the company's performance after the acquisition based on the input information; means for displaying the prediction results on the user interface; means for executing an emotion recognition engine that analyzes emotion data input from the user interface; means for customizing the display content and proposal method of the user interface based on the emotion data; means for automatically generating a transaction contract and performing legal checks based on the information and prediction results; means for generating advice to avoid problems by referring to past trouble cases; and means for proposing optimal acquisition and sale matching. This makes each step of the M&A process more efficient, significantly reduces uncertainty, and alleviates user stress and anxiety, enabling appropriate decision-making.

[1768] A "user interface" is an interface through which a user inputs information and checks results.

[1769] An "artificial intelligence model" is a model that uses machine learning algorithms to analyze data and generate predictive results.

[1770] An "emotion recognition engine" is an engine that analyzes emotions in real time from data such as a user's facial expressions and voice.

[1771] A "commercial agreement" is a legal document that sets out the agreed-upon terms and conditions of a commercial transaction.

[1772] "Legal check" is the process of verifying whether a transaction contract meets legal requirements.

[1773] The "trouble case database" is a database that records details of past troubles and their solutions.

[1774] "Matching" is the process of proposing a buyer or seller based on specific criteria.

[1775] The following system and method are used as an embodiment of this invention: The system inputs information about the company and the target company, predicts the company's performance after the acquisition, performs sentiment analysis, automatically generates transaction contracts and performs legal checks, provides advice by referring to trouble cases, and proposes optimal matching.

[1776] The system consists of the following main modules:

[1777] 1. Providing a user interface

[1778] The user uses a terminal to input detailed data about their own company and the target company (financial data, business operations, number of employees, etc.). This information is sent to the server, validated, and then stored in a database. The user interface is built using web technologies such as HTML, CSS, and JavaScript.

[1779] 2. AI-based corporate performance prediction

[1780] The server sends the stored company information to an AI analysis module to predict the company's performance after the acquisition. The AI ​​module uses machine learning libraries such as TensorFlow and Keras to generate predictions such as sales growth rate, cost reduction effect, and profit margin. This makes it possible to predict the company's performance.

[1781] 3. Emotion analysis using an emotion recognition engine

[1782] While the user is entering information on the device or checking prediction results, the emotion recognition engine analyzes the user's facial expressions and input data. The emotion recognition engine uses OpenCV and TensorFlow to perform image processing and emotion analysis. For example, emotions such as anxiety, excitement, and relief are detected in real time and the data is sent to the server.

[1783] 4. Emotional customization

[1784] The server receives data from the emotion engine and customizes the content and suggestions displayed in the user interface. For example, if the user is feeling anxious, it can provide more detailed explanations or additional reassuring information, thereby reducing the user's stress and anxiety.

[1785] 5. Automatic generation and legal check of transaction contracts

[1786] The user selects the automatic contract generation function based on the prediction results. The server generates a customized contract based on the template contract, reflecting the user information and analysis results, and then checks legal requirements using a legal check module. The legal check module is composed of algorithms to meet various legal regulations and compliance requirements.

[1787] 6. Providing advice to avoid problems

[1788] The server refers to a database of past trouble cases and searches for similar trouble cases. Based on this, it generates advice for avoiding trouble and displays it on the user interface. For example, based on information that "in cases where problems occurred under similar conditions, there were delays in payment after the contract was signed," it provides advice on setting strict payment terms.

[1789] 7. Proposal of optimal matching

[1790] The server proposes optimal acquisition and sale matching based on the user's conditions and analysis results. The matching results are generated in report format and delivered to the user interface. The user can confirm this and proceed with the final transaction.

[1791] Specific examples

[1792] For example, in factory operations, when a user inputs production data and the number of employees, AI predicts production efficiency and cost reduction effects. Furthermore, an emotion recognition engine can detect employee dissatisfaction and display a message on the user interface saying, "Employee dissatisfaction detected. Action required." This allows appropriate action to be taken.

[1793] Prompt Sentence Examples

[1794] "We've detected employee dissatisfaction. Would you like to suggest improvements to maintain efficiency at 85%?"

[1795] "Employee satisfaction is high. Do you want to stick with the current plan?"

[1796] "The employee's status is neutral. Would you like to consider further improvements?"

[1797] In this way, the entire system can provide highly personalized support on the user's terms, making the M&A process efficient and effective.

[1798] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1799] Step 1:

[1800] The user uses a terminal to input detailed data such as financial data, business operations, and number of employees of their own company and the target company. The input information is sent to the server, where it is validated and stored in a database. The input data validation process checks the data format and checks for required fields.

[1801] Step 2:

[1802] The server sends data to an artificial intelligence model based on the stored company information to predict the company's performance after the acquisition. Specifically, it uses machine learning libraries such as TensorFlow and Keras to generate predictions such as sales growth rate, cost reduction effect, and profit margin. These prediction results are generated in JSON format and sent to the user interface.

[1803] Step 3:

[1804] The user checks the prediction results on the device. At this stage, the user's facial expressions and voice are collected through the camera and microphone and sent to the server. The emotion recognition engine analyzes this data using OpenCV and TensorFlow to detect emotions such as anxiety, excitement, and relief in real time. The analysis results are then sent to the server.

[1805] Step 4:

[1806] The server customizes the content and suggestions displayed in the user interface based on the emotion data from the emotion recognition engine. For example, if the user feels anxious, the server can display more detailed explanations and additional information to reassure the user. This can reduce the user's stress and anxiety.

[1807] Step 5:

[1808] After checking the prediction results, the user selects the automatic generation function for the transaction contract. The server generates a customized contract based on the template contract, reflecting the user information and analysis results. Furthermore, a legal check module is used to check legal requirements. This process includes an algorithm that uses natural language processing technology to check whether each clause in the contract meets legal requirements.

[1809] Step 6:

[1810] The server refers to a database of past trouble cases and searches for similar trouble cases. For example, based on information such as "in cases where problems occurred under similar conditions, there were delays in payments after the contract was signed," it generates advice for avoiding trouble. This advice is displayed on the user interface to help the user take appropriate action.

[1811] Step 7:

[1812] The server proposes optimal acquisition and sale matching based on the user's criteria and analysis results. The matching results are generated in report format and delivered to the user interface. The user can review this and proceed with the final transaction. The matching algorithm is designed to select the optimal partner based on input data and past success stories.

[1813] Through these steps, the entire system provides highly personalized support, streamlining the M&A process and reducing the psychological burden on users.

[1814] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1815] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1816] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1817] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1818] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1819] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1820] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1821] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1822] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1823] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1824] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1825] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1826] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1827] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1828] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1829] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1830] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1831] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1832] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1833] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1834] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1835] The following is further disclosed regarding the above embodiment.

[1836] (Claim 1)

[1837] A means for providing a user interface for inputting information about the company and the target company;

[1838] A means for executing an artificial intelligence model that predicts the performance of the company after the acquisition based on the input information;

[1839] means for displaying the prediction result on a user interface;

[1840] A means for automatically generating a transaction contract and performing legal checks based on the information and the prediction results;

[1841] A means of referring to past trouble cases and generating advice to avoid trouble,

[1842] A mean...

Claims

1. A means for providing a user interface for inputting information about the company and the target company; A means for executing an artificial intelligence model that predicts the performance of the company after the acquisition based on the input information; means for displaying the prediction result on a user interface; A means for automatically generating a transaction contract and performing legal checks based on the information and the prediction results; A means of referring to past trouble cases and generating advice to avoid trouble, A means of proposing optimal acquisition and sale matching, A system including:

2. 10. The system of claim 1, wherein the means for performing a legal check includes an algorithm for verifying legal compliance of a transaction agreement.

3. 2. The system according to claim 1, wherein said means for providing advice for avoiding trouble refers to a database of past trouble cases and proposes solutions under similar conditions.

Images